Examples of 'task' in a sentence
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Task in a Sentence 🔊
Definition of Task
work to be done or completed
Examples of Task in a sentence
My task is to organize all of these papers before noon, but after that I can take a short break. 🔊
My mother gave me the task of mopping the floors before she got home from the grocery store, something I really hate doing. 🔊
Homework is a task that students are expected to complete on their own time, but sometimes they do it while still at school. 🔊
My teacher gave me the important task of delivering the class attendance sheet to the office for her. 🔊
The general gave his scout an important task to investigate enemy positions so he could make plans for the assault on the enemy position. 🔊
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- Cambridge Dictionary +Plus
Meaning of task in English
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task noun ( WORK )
- act as something
- all work and no play (makes Jack a dull boy) idiom
- be at work idiom
- hot-desking
- housekeeping
- in the line of duty idiom
- short-staffed
- undertaking
task noun ( SPEAK ANGRILY )
- all-points bulletin
- boss someone around
- put something on
- self-ordained
- shove someone around
- stick out for something
task | American Dictionary
Task | business english, examples of task, collocations with task.
These are words often used in combination with task .
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Translations of task
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a game played by two or more children in which one child chases the others and tries to touch one of them. This child then becomes the one who does the chasing.
Infinitive or -ing verb? Avoiding common mistakes with verb patterns (1)
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- task (WORK)
- task (SPEAK ANGRILY)
- take someone to task
- American Noun
- take sb/sth to task (over sth)
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English sentences focusing on words and their word families the word "task" in example sentences page 1.
“Task” in a Sentence (with Audio)
Examples of how to use the word “task” in a sentence. How to connect “task” with other words to make correct English sentences.
task (n): a piece of work to be done, especially one done regularly, unwillingly, or with difficulty
Use “task” in a sentence
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Can 'Task' Be Used as a Verb?
No one likes tasks . This is unsurprising, given that the definitions we give for this noun include “a usually assigned piece of work often to be finished within a certain time,” “subjection to adverse criticism,” and “something hard or unpleasant that has to be done.” You will rarely hear someone speak of the “lovely task” they’ve just been given. But some are not content with disliking this word based on its semantic content, and have taken additional umbrage with its use as a verb.
Could have sworn there was something else we had to do...
It is uncertain exactly why this distaste for the transitive verb form of task has come about. Some possibilities are that it is seen as being new, that it is business jargon, or that it frequently is found used in the passive voice. Prohibitions against the verb are rarely found in usage and style guides, and when they are encountered few seem to take them very seriously.
task is not a verb. — Telegraph Style Book; The official guide to house style for The Daily Telegraph , (https://www.telegraph.co.uk/style-book/t/) Ronald Koeman tasked with resuscitating mediocre Dutch national team and own management career — (headline) The Daily Telegraph (London, Eng.), 22 Mar. 2018 A unit of US special forces tasked with carrying out “decapitation” operations may be aboard a nuclear-powered submarine docked in the South Korean port of Busan, the nation’s newswire reported on Monday, citing a defence source. — The Daily Telegraph (London, Eng.), 17 Oct. 2017 Pippa Grange, 47, is tasked with changing the culture and mindset of England teams, and increasing their “psychological resilience” to the pressure of winning critical matches.… — The Daily Telegraph (London, Eng.), 4 Jan. 2018
Task is not a new verb. In fact, it has been verbing along since the 14th century, used with the meaning of “to assign a task to.” It also has an obsolete sense of “to impose a tax on,” and an additional current meaning of “to oppress with great labor.” The word has shown an increase in use of late, particularly in business writing (a form of English many people take great dislike to), but getting tasked with something is commonly found in military use from at least the 1960s.
One consolidated handbook covering the subjects of welfare organizations, pay and allowances, allotments, travel, shipment of household effects, overseas duty stations, disabilitv separation, retirement, promotion, reenlistment benefits, medical care, survivors' benefits-and others-would answer the prayers of those tasked with these additional duties. — Leatherneck (Quantico, VA), Oct. 1963 The platoon is tasked to conduct a two-week platoon sweep between Hue and Danang with the objective of eliminating all enemy forces within the Hue-Danang axis, the bush country as well as Route One. — Marine Corps Gazette , May 1970 Responsibility for delivering the food to Yuma was tasked to Marine Aerial Refueler-Transport Squadron 252. — Leatherneck (Quantico, VA), Jul. 1971
This particular turn of phrase did not originate with the military, and may be seen on occasion in much earlier examples.
The establishment involved immense expenses, and responsibilities, and was tasked with the transmission not only of intelligence, but of immense amounts of exchanges. — Niles’ National Register (Baltimore, MD), 19 Dec. 1846 It was impossible for me to write a line all this week, as I was on every committee which was tasked with receiving and entertaining Benjamin Harrison, President of the United States. — The American Israelite (Cincinnati, OH), 7 May 1891
It is fine with us if you wish to avoid using the passive voice in your writing, and we also have no problem with you eschewing the use of task as a verb. But any decent peeve should have a solid foundation, and saying that task is not a verb is deficient in that regard. Perhaps you could go with the old standby of “I just hate that word.”
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Grammar: sentence structure and types of sentences, definitions and examples of basic sentence elements.
The Mastering the Mechanics webinar series also describes required sentence elements and varying sentence types. Please see these archived webinars for more information.
Key: Yellow, bold = subject; green underline = verb, blue, italics = object, pink, regular font = prepositional phrase
Independent clause : An independent clause can stand alone as a sentence. It contains a subject and a verb and is a complete idea.
- I like spaghetti .
- He reads many books .
Dependent clause : A dependent clause is not a complete sentence. It must be attached to an independent clause to become complete. This is also known as a subordinate clause.
- Although I like spaghetti,…
- Because he reads many books,…
Subject : A person, animal, place, thing, or concept that does an action. Determine the subject in a sentence by asking the question “Who or what?”
- I like spaghetti.
- He reads many books.
Verb : Expresses what the person, animal, place, thing, or concept does. Determine the verb in a sentence by asking the question “What was the action or what happened?”
- The movie is good. (The be verb is also sometimes referred to as a copula or a linking verb. It links the subject, in this case "the movie," to the complement or the predicate of the sentence, in this case, "good.")
Object : A person, animal, place, thing, or concept that receives the action. Determine the object in a sentence by asking the question “The subject did what?” or “To whom?/For whom?”
Prepositional Phrase : A phrase that begins with a preposition (i.e., in, at for, behind, until, after, of, during) and modifies a word in the sentence. A prepositional phrase answers one of many questions. Here are a few examples: “Where? When? In what way?”
- I like spaghetti for dinner .
- He reads many books in the library .
English Sentence Structure
The following statements are true about sentences in English:
- H e obtained his degree.
- He obtained his degree .
- Smith he obtained his degree.
- He obtained his degree.
- He (subject) obtained (verb) his degree (object).
Simple Sentences
A simple sentence contains a subject and a verb, and it may also have an object and modifiers. However, it contains only one independent clause.
Key: Yellow, bold = subject; green underline = verb, blue, italics = object, pink, regular font =prepositional phrase
Here are a few examples:
- She wrote .
- She completed her literature review .
- He organized his sources by theme .
- They studied APA rules for many hours .
Compound Sentences
A compound sentence contains at least two independent clauses. These two independent clauses can be combined with a comma and a coordinating conjunction or with a semicolon .
Key: independent clause = yellow, bold ; comma or semicolon = pink, regular font ; coordinating conjunction = green, underlined
- She completed her literature review , and she created her reference list .
- He organized his sources by theme ; then, he updated his reference list .
- They studied APA rules for many hours , but they realized there was still much to learn .
Using some compound sentences in writing allows for more sentence variety .
Complex Sentences
A complex sentence contains at least one independent clause and at least one dependent clause. Dependent clauses can refer to the subject (who, which) the sequence/time (since, while), or the causal elements (because, if) of the independent clause.
If a sentence begins with a dependent clause, note the comma after this clause. If, on the other hand, the sentence begins with an independent clause, there is not a comma separating the two clauses.
Key: independent clause = yellow, bold ; comma = pink, regular font ; dependent clause = blue, italics
- Note the comma in this sentence because it begins with a dependent clause.
- Note that there is no comma in this sentence because it begins with an independent clause.
- Using some complex sentences in writing allows for more sentence variety .
Compound-Complex Sentences
Sentence types can also be combined. A compound-complex sentence contains at least two independent clauses and at least one dependent clause.
Key: independent clause = yellow, bold ; comma or semicolon = pink, regular font ; coordinating conjunction = green, underlined ; dependent clause = blue, italics
- She completed her literature review , but she still needs to work on her methods section even though she finished her methods course last semester .
- Although he organized his sources by theme , he decided to arrange them chronologically , and he carefully followed the MEAL plan for organization .
- With pizza and soda at hand , they studied APA rules for many hours , and they decided that writing in APA made sense because it was clear, concise, and objective .
- Using some complex-compound sentences in writing allows for more sentence variety .
- Pay close attention to comma usage in complex-compound sentences so that the reader is easily able to follow the intended meaning.
Sentence Structure Video Playlist
Note that these videos were created while APA 6 was the style guide edition in use. There may be some examples of writing that have not been updated to APA 7 guidelines.
- Structuring Sentences: Types of Sentences (video transcript)
- Structuring Sentences: Simple Sentences (video transcript)
- Structuring Sentences: Compound Sentences (video transcript)
- Structuring Sentences: Complex Sentences (video transcript)
- Structuring Sentences: Combining Sentences (video transcript)
- Common Error: Unclear Subjects (video transcript)
- Mastering the Mechanics: Punctuation as Symbols (video transcript)
- Mastering the Mechanics: Commas (video transcript)
- Mastering the Mechanics: Periods (video transcript)
- Mastering the Mechanics: Semicolons (video transcript)
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TAKE TO TASK in a Sentence Examples: 21 Ways to Use Take To Task
Have you ever wanted to hold someone accountable or criticize them for their actions? When you “take to task” someone, you confront them about their behavior or performance, often to point out their mistakes or shortcomings.
This phrase implies a level of authority or responsibility in correcting the person’s actions or addressing the issue at hand. When you “take to task” someone, you are actively calling them out and ensuring that they are aware of their errors or misconduct.
Table of Contents
7 Examples Of Take To Task Used In a Sentence For Kids
- Take to task means to scold someone.
- I will take to task my friend if he doesn’t share his toys.
- The teacher will take to task the student who is not paying attention in class.
- Parents may take to task their children if they misbehave.
- We should not take to task others for mistakes they didn’t make.
- It is important to take to task people in a gentle and respectful way.
- Sometimes, it is necessary to take to task someone to correct their behavior.
14 Sentences with Take To Task Examples
- College professors may *take to task students who do not submit their assignments on time. *
- If a student is caught cheating during an exam, the academic committee will certainly take them to task .
- Group projects require effective communication and teamwork; otherwise, the group leader may take to task * uncooperative members. *
- Plagiarism is a serious offense in academia, and universities often take to task * students who are found guilty of it. *
- Latecomers to class may be *taken to task by strict professors for disrupting the lecture. *
- If a student is consistently disruptive in class, the disciplinary committee will have to *take them to task . *
- Failure to adhere to the dress code in college may result in being taken to task * by the administrative staff. *
- Academic dishonesty, such as fabricating research data, can lead to being taken to task * by the research supervisor. *
- Skipping classes without a valid reason can ultimately lead to being taken to task * by the Dean of the college. *
- Participating in unruly behavior during college events can prompt the student council to *take to task those involved. *
- Engaging in bullying or harassment towards fellow students may result in being taken to task * by the anti-ragging committee. *
- Mismatching research data in a thesis may lead to being *taken to task by the thesis committee. *
- Planned activities of the college clubs need full student participation; otherwise, the club president may take to task * those who do not contribute. *
- Persistent academic underperformance could potentially take a student to task * during their review with the academic advisor. *
How To Use Take To Task in Sentences?
To use Take To Task in a sentence, start by identifying a situation where someone needs to be held accountable for their actions or behavior. Next, ensure that you have a clear understanding of the specific actions or behavior that you want to address. Then, construct a sentence that directly addresses the individual and highlights the behavior that is being called into question.
For example, you could say, “I need to take you to task for not completing your assigned project on time.” In this sentence, you are directly addressing the individual and pointing out the specific behavior (not completing the project on time) that needs to be addressed.
When using Take To Task , it is important to be firm and direct in your communication. Avoid using vague language or beating around the bush. Clearly state the behavior that is unacceptable and explain why it is problematic. This will help ensure that the individual understands why they are being held accountable and what they need to do to rectify the situation.
Remember to remain professional and composed when using Take To Task in a sentence. It is important to address the behavior in a constructive and respectful manner to promote positive communication and resolution.
Therefore, when we talk about taking someone to task, we mean holding them accountable or criticizing them for their actions or behavior. For example, the boss took the employee to task for missing the deadline, or the teacher took the student to task for not completing the assignment. This phrase denotes a reprimand or formal criticism that aims to address a mistake or wrongdoing.
Overall, taking someone to task involves confronting them about their faults or errors and ensuring that they are held responsible for their actions. It serves as a way to provide feedback, correct behavior, and maintain accountability in various contexts, whether at work, school, or in personal relationships.
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Frequently asked questions
The act of putting someone else’s ideas or words into your own words is called paraphrasing, rephrasing, or rewording. Even though they are often used interchangeably, the terms can mean slightly different things:
Paraphrasing is restating someone else’s ideas or words in your own words while retaining their meaning. Paraphrasing changes sentence structure, word choice, and sentence length to convey the same meaning.
Rephrasing may involve more substantial changes to the original text, including changing the order of sentences or the overall structure of the text.
Rewording is changing individual words in a text without changing its meaning or structure, often using synonyms.
It can. One of the two methods of paraphrasing is called “Fluency.” This will improve the language and fix grammatical errors in the text you’re paraphrasing.
Paraphrasing and using a paraphrasing tool aren’t cheating. It’s a great tool for saving time and coming up with new ways to express yourself in writing. However, always be sure to credit your sources. Avoid plagiarism.
If you don’t properly cite text paraphrased from another source, you’re plagiarizing. If you use someone else’s text and paraphrase it, you need to credit the original source. You can do that by using citations. There are different styles, like APA, MLA, Harvard, and Chicago. Find more information about citing sources here.
Paraphrasing without crediting the original author is a form of plagiarism , because you’re presenting someone else’s ideas as if they were your own.
However, paraphrasing is not plagiarism if you correctly cite the source . This means including an in-text citation and a full reference, formatted according to your required citation style .
As well as citing, make sure that any paraphrased text is completely rewritten in your own words.
Plagiarism means using someone else’s words or ideas and passing them off as your own. Paraphrasing means putting someone else’s ideas in your own words.
So when does paraphrasing count as plagiarism?
- Paraphrasing is plagiarism if you don’t properly credit the original author.
- Paraphrasing is plagiarism if your text is too close to the original wording (even if you cite the source). If you directly copy a sentence or phrase, you should quote it instead.
- Paraphrasing is not plagiarism if you put the author’s ideas completely in your own words and properly cite the source .
- Artificial Intelligence
Google Chrome’s new AI can finish your sentences for you
The experimental ai feature is available in english for us-based chrome users, providing suggestions for completing online reviews, forms, messages, and more..
By Jess Weatherbed , a news writer focused on creative industries, computing, and internet culture. Jess started her career at TechRadar, covering news and hardware reviews.
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Google has started rolling out “Help me write” — an experimental Gemini-powered generative AI feature for its Chrome browser that aims to help users write or refine text based on webpage content. Following the stable release of Chrome M122 on Tuesday, the new writing assistant is now available to try out on Mac and Windows PCs for English-speaking Chrome users in the US.
“Help me write” focuses on providing writing suggestions for shortform content, such as filling in digital surveys and reviews, enquiring about product information, or drafting descriptions for items being sold online. Google says the tool can “understand the context of the webpage you’re on” to pull relevant information into its suggestions — for example, highlighting key features mentioned on the product page for items you’re leaving a review on.
The “Help me write” feature has undergone some visual changes since it was first announced for Gmail during Google’s I/O event last May , now appearing as a floating application window beside the webpage text fields that are being filled with separate options to adjust length and tone. The Chrome release offers similar functionality to what Microsoft released for Edge and Bing search last year .
Users in the US will need to enable Chrome’s Experimental AI to use the feature, which can be found by clicking on Settings within the three-dot drop-down menu on Chrome desktop and then navigating to the Experimental AI page. From there, click on “Try out experimental AI features” and select “Help me write” and then “relaunch.” Users can then navigate to a webpage on Chrome and right-click on an open text field to use the writing assistant feature.
The Google support page includes a disclaimer that tells users not to provide personal information like their name, phone, address, social security number, or credit card information to the feature and that the tool shouldn’t be used on websites that contain personal or sensitive information. But if you do input such information, Google says that “Chrome will not use it for model training purposes.”
I’m not convinced the “Help me write” tool will prove very useful for most people — it’s not exactly a must-have feature driving the adoption of Edge and Copilot over the last year. The use cases provided by Google seem reasonable if the feature spits out the exact copy you need, but any time spent writing the prompts and adjusting the resulting text to suit your needs diminishes any time-saving benefits it may have provided. I can see some benefits for disabled users or people who aren’t completely fluent in English, but there’s also plenty to be concerned about — the ease with which this tool could be used to leave fake or disingenuous product reviews being one of them.
The AIs are officially out of control
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OpenAI teases ‘Sora,’ its new text-to-video AI model
Want to see a turtle riding a bike across the ocean ? Now, generative AI can animate that scene in seconds.
OpenAI on Thursday unveiled its new text-to-video model Sora, which can generate videos up to a minute long based on whatever prompt a user types into a text box. Though it’s not yet available to the public, the AI company’s announcement roused a frenzy of reactions online.
AI enthusiasts were quick to brainstorm ideas around the potential of this latest technology, even as others raised immediate concern over how its accessibility might erode human jobs and further the spread of digital disinformation.
OpenAI CEO Sam Altman solicited prompt ideas on X and generated a series of videos including the aforementioned aquatic cyclists, as well as a cooking video and a couple of dogs podcasting on a mountain.
“We are not making this model broadly available in our products soon,” a spokesperson for OpenAI wrote in an email, adding that the company is sharing its research progress now to gain early feedback from others in the AI community.
The company, with its popular chatbot ChatGPT and text-to-image generator DALL-E, is one of several tech startups leading the generative AI revolution that began in 2022. It wrote in a blog post that Sora can generate with accuracy multiple characters and different types of motion.
“We’re teaching AI to understand and simulate the physical world in motion, with the goal of training models that help people solve problems that require real-world interaction,” OpenAI wrote in the post.
But Sora may struggle to capture the physics or spatial details of a more complex scene, which can lead it to generate something illogical (like a person running in the wrong direction on a treadmill), morph a subject in unnatural ways, or even cause it to disappear out of thin air, the company said in its blog post .
Still, many of the demonstrations shared by OpenAI showcased hyper-realistic visual details that could make it difficult for casual internet users to distinguish AI-generated video from real-life footage. Examples included a drone shot of waves crashing into a craggy Big Sur coastline under the glow of a setting sun and a clip of a woman strolling down a bustling Tokyo street still damp with rain.
As deepfaked media of celebrities, politicians and private figures becomes increasingly prevalent online, the ethical and safety implications of a world in which anyone can create high-quality video of anything they can imagine — especially during a presidential election year, and amid tense global conflicts fraught with opportunities for disinformation — are daunting.
The Federal Trade Commission on Thursday proposed rules aimed at making it illegal to create AI impressions of real people by extending protections it is putting in place around government and business impersonation.
“The agency is taking this action in light of surging complaints around impersonation fraud, as well as public outcry about the harms caused to consumers and to impersonated individuals,” the FTC wrote in a news release. “Emerging technology — including AI-generated deepfakes — threatens to turbocharge this scourge, and the FTC is committed to using all of its tools to detect, deter, and halt impersonation fraud.”
OpenAI said it is working to build tools that can detect when a video is generated by Sora, and plans to embed metadata, which would mark the origin of a video, into such content if the model is made available for public use in the future.
The company also said it is collaborating with experts to test Sora for its ability to cause harm via misinformation, hateful content and bias.
A spokesperson for OpenAI told NBC News it will then publish a system card describing its safety evaluations, as well as the model’s risks and limitations.
“Despite extensive research and testing, we cannot predict all of the beneficial ways people will use our technology, nor all the ways people will abuse it,” OpenAI said in its blog post. “That’s why we believe that learning from real-world use is a critical component of creating and releasing increasingly safe AI systems over time.”
Angela Yang is a culture and trends reporter for NBC News.
APRE: Annotation-Aware Prompt-Tuning for Relation Extraction
- Open access
- Published: 21 February 2024
- Volume 56 , article number 62 , ( 2024 )
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- Chao Wei 1 , 2 ,
- Yanping Chen 1 , 2 ,
- Kai Wang 1 , 2 ,
- Yongbin Qin 1 , 2 ,
- Ruizhang Huang 1 , 2 &
- Qinghua Zheng 3
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Prompt-tuning has been successfully applied to support classification tasks in natural language processing and has achieved promising performance. The main characteristic of prompt-tuning based classification is to verbalize class labels and predict masked tokens like a cloze-like task. It has the advantage to make use of knowledge in pre-trained language models (PLMs). Because prompt templates are manually designed, they are more prone to overfitting. Furthermore, traditional prompt templates are appended in the tail of an original sentence. They are far from some semantic units in a sentence. It is weak to decode semantic information of an input relevant to PLMs. To aggregate more semantic information from PLMs for masked token prediction, we propose an annotation-aware prompt-tuning model for relation extraction. In our method, entity type representations are used as entity annotations. They are implanted near the site of entities in a sentence for decoding semantic information of PLMs. It is effective to make full use of knowledge in PLMs for relation extraction. In the experiment section, our method is validated on the Chinese literature text and SemEval 2010 Task datasets and achieves 89.3% and 90.6% in terms of F1-score, respectively. It achieves the state-of-the-art performance on two public datasets. The result further demonstrates the effectiveness of our model to decode semantic information in PLMs.
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1 Introduction
Relation extraction (RE) is a fundamental task in information extraction, which identifies semantic relationships between two named entities within a sentence. It was widely adopted to construct Knowledge Graphs (KG) and support applications, such as information extraction, question answering, and construction of semantic networks. Extracting entity relations is key to understand semantic meaning of a sentence. Therefore, the task has received great attention in recurrent years [ 1 , 2 , 3 , 4 ]. Despite the great success that has been achieved in this field, it still remains a challenging task due to the reason that a sentence usually contains very little information, which leads to a serious sparse features.
Pre-trained Language Models (PLMs) are trained from external resources with unsupervised methods. PLMs usually consist of billions of parameters automatically learned from external resources, which encode rich knowledge of sentences that are valuable for relation extraction. They are effective to learn semantic information of tokens and the semantic dependencies of a sentence. The performance of many tasks in Natural Language Processing (NLP) has been substantially improved by the emergence of PLMs, such as BERT, GPT3 and etc. [ 5 , 6 ]. Supported by PLMs,relation extraction tasks also achieved state-of-the-art results in many benchmarks [ 7 , 8 ].
The methods to utilize PLMs can be roughly divided into two categories: fine-tuning and prompt tuning. In fine-tuning models, PLMs is used as a lookup table, which maps each token into a dense vector encoded with pre-trained semantic information. Then, a classifier is trained to make a classification based on abstract representations of a sentence. In the training process, PLMs are also tuned according to the classification objectives. In prompt tuning models, the classification is implemented as a cloze-like task, where class labels are verbalized and predicted as masked tokens. In prompt tuning models, prompt templates with slots are used to decode semantic information from PLMs. An example of fine-tuning and prompt tuning is shown in the middle of Fig. 1 , where the original sentence “The sergeant got out of the car.” contains two named entities “sergeant” and “car”.
Examples of fine-tuning and prompt-tuning for Relation Extraction. The yellow rectangles in the figure are special tokens. (Color figure online)
The top of Fig. 1 is a fine-tuning approach, where the output of PLMs is directly fed into a classifier, which generates confidence scores for each relation type. In the middle of Fig. 1 , a prompt template “sergeant [MASK] car” is employed and concatenated with the original sentence. Then, they are fed into PLMs to predicate the masked token slots. The process to predict prompt template slots is the same as masked language model training. If it outputs “origin” verbalize class tokens, it indicates a “Entity-Origin” relation between “segeant” and “car”. Compared to fine-tuning models, prompt tuning has the advantage to make full use of knowledge in pre-trained language models (PLMs) and bridge the gap between pre-training objectives and fine-tuning objectives. Therefore, in recent years, prompt-tuning has been widely applied to support relation extraction [ 9 , 10 , 11 , 12 , 13 ].
Even great success has been achieved in prompt tuning models. Current RE models with prompt-tuning still confronts two significant challenges. First, because prompt templates are manually designed, it is easy to overfit the evaluation dataset. The migration between different domains is difficult. Second, previous prompt-tuning methods do not adequately utilize contextual features of a sentence. These models simply stitching prompts into the tail of sentences are ineffective to capture the contextual features of a sentence. It hinders the learning of the context of the entity in masked positions. For example, the type of “car” may be “destination” or “origin.” And the type of “car” is ambiguous according to the previous prompt-tuning methods. However, as shown in the bottom of Fig. 1 , we can intuitively imply from the semantics of “got out of the car” that the type of “car” is “origin”. In other words, the context contains many significant elements that help to understand relations among relation instances.
To aggregate more semantic information from PLMs for masked token prediction, we propose an annotation-aware prompt-tuning model for relation extraction (APRE). In our method, instead of manually designed prompt templates, entity annotations, implanted within a sentence, are adopted to decode semantic information of PLMs. Entity annotations are entity type representations, which contains semantic information about named entities. Instead of appending to the tail of a sentence by prompt templates, annotations are used as supplement features of named entities and implanted with named entities for semantic disambiguation. Because these annotations are near named entities in a sentence, it is effective to lean contextual features and semantic dependencies of the sentence. It also has the advantage to utilize potential knowledge of PLMs. We summarize our work contributions as follows:
We propose an Annotation-aware Prompt tuning for Relation Extraction that transforms prompts into annotation with contextual features. By introducing annotation prompts, the model can decode semantic information of PLMs.
Our proposed approach achieves state-of-the-art performance on two public datasets, SemEval (English) and CLTC (Chinese). Experimental results demonstrate the advancement and scalability of our method.
The rest of this paper is mainly divided into four different sections. Section 2 discusses the related works on RE. Section 3 details the annotation prompt. Section 4 shows the evaluation of the annotation prompt on Chinese and English datasets and presents the relevant experimental analysis. The conclusion is given in Sect. 5 .
2 Related Work
The task of RE is usually regarded as a classification problem, which makes a predication based on features about an entity pair of a relation instance. In recent years, PLMs such as GPT [ 6 ], BERT [ 5 ], and RoBERTa [ 14 ] have been widely adopted to support RE. These PLMs are trained by leveraging a large-scale unlabeled data, which provides rich semantic knowledge for the RE task [ 15 ]. In this section, we divided related work to utilize PLMs for RE into two categories: fine-tuning based models and prompt-tuning based models.
Fine-tuning based models are the traditional strategy to utilize PLMs. It has achieved great success, and the performance of RE has been improved considerably [ 8 , 16 , 17 , 18 , 19 , 20 ]. Since a relation is defined as a semantic relationship relevant to two named entities in a sentence, related works try to learn a structural sentence representation relevant to two named entities. For example, Wu and He [ 16 ] utilize a pre-trained architecture to locate target entities for enhancing sentence representations with encodings of entities. They propose an R-BERT model that could utilize information among PLMs and entities.
Because a sentence usually contains multiple entity pairs that share the same contextual features, it is also important to encode structural information of a relation instance. Chen et al. [ 18 ] introduced a neuralized feature engineering method that leverages manually designed features to improve the ability of neural networks for learn structural features. Zhao et al. [ 21 ] proposed a relation extraction method based on heterogeneous graph neural networks. They represent words and relations as nodes in a graph, which can iteratively obtain more appropriate node representations by message passing mechanisms. Zhou and Chen [ 20 ] utilize entity representations with TYPE MARKER to improve the baseline model performance. This approach virtually mitigates the influence of incorrectly defined labels.
Compared to general fine-tuning methods, knowledge-enhanced PLMs have significant performance improvements. Soares et al. [ 8 ] introduce extra knowledge via entity-linked text to construct agnostic task relation representations. They retrieve entity embeddings using entity links and update the context representation among words and entities. By building multiple knowledge bases of structured and human-curated knowledge, Peters et al. [ 17 ] have further enhances the knowledge representations of the model. The above methods of fine-tuning PLMs have proven to be successful on multiple datasets.
The prompt-tuning strategy is proposed in GPT-3 [ 6 ]. It has attracted the attention of researchers in various fields in recent years. This strategy leverages the prompt as context to alleviate the gap among tasks and PLMs by representing downstream tasks as pre-trained tasks. Recently, a series of studies [ 7 , 12 , 13 , 22 , 23 , 24 ] have demonstrated the effectiveness of PLMs. Because prompts have the ability to decode knowledge of PLMs, it achieved excellent performance on multiple NLP tasks. Ding et al. [ 22 ] aim to construct prompt templates and entity-oriented verbalizers. Then, they are utilized to explore entity types in prompt-tuning. Schick et al. [ 10 ] propose a method to avoid the laborious prompt design by studying the automatic exploration process of templates and answer words. Li and Liang [ 23 ] embed discrete template words as learnable continuous words, making the connection between template words closer.
During the process to design prompt templates, manual design is time-consuming and laborious. On the other hand, automated searches are resource-intensive. Hence, to support relation classification, Han et al. [ 12 ] construct prompts with multiple sub-prompts based on logical rules and proposed prompt tuning with rules (PTR). This method effectively reduces the labor costs caused by manual design and the waste of resources generated by automatic generation. Chen et al. [ 7 ] adopt a knowledge-aware approach with synergistic optimization in prompt tuning. They inject knowledge into the learnable continuous words, so as to the template words and answer words can acquire relevant knowledge during the prompt-tuning. It optimizes the representation of the prompt of the virtual template and answers words under the constraints of knowledge.
3 Methodology
Before introducing our method, we first give a formalized discussion about fine-tuning and prompt-tuning in relation extraction.
A relation instance is defined as a 3-triple \( I =\ \left\langle e_{s},e_{o}, r \right\rangle \) . Where \(e_{s}\) and \(e_{o}\) denote to two named entities, \( r \) denotes to a relation mention, which consists of a token sequence \( r \ = \ [t_1,t_2,\ldots ,t_n]\) . Named entity \(e\ =\ [t_i,\ldots ,t_j]\ (e\in \{e_s,e_o\})\) is a sub-sequence of \( r \) . Meanwhile, let \(\textbf{Y}\ =\ \{y_0,y_1,\ldots ,y_n\}\) be a set of relation categories, where \(y_0\) represents a negative relation type. \(\textbf{I}\ =\ \{I_1,I_2,\ldots ,I_m\}\) represent a relation instance set. Then, the task of RE can be expressed as a mapping process between \(\textbf{I}\) and \(\textbf{Y}\) .
Previous fine-tuning methods first utilize a pre-trained language model to map an input relation mention \( r= \{w_{\mathrm {\texttt {[CLS]}}}, w_1,w_2, \ldots w_s, \ldots ,w_o \ldots ,w_n,w_{\mathrm {\texttt {[SEP]}}}\}\) into abstract representation. The mapping has the ability to learn semantic features and contextual dependencies between tokens in a relation mention. The process is formalized as follows:
Generally, in fine-tuning methods, the output layer is a classifier composed of a multilayer perceptron layer and a softmax layer, which outputs the probability distribution of I on the relation label set \(\textbf{Y}\) :
where \(\mathrm {{\textbf {W}}}\) and \({\textbf {b}}\) are learnable parameters, \(\textrm{H}_{\mathrm {\texttt {[CLS]}}}\) is the output vector of \(\mathrm {\texttt {[CLS]}}\) , which can be used as the abstract representation of r . \(p(y\mid x)\) ( \(y\in \textbf{Y}\) ) represents the probability distribution under each category. Finally, the model utilizes the entire training set to minimize the cross-entropy loss to tune parameters.
On the other hand, prompt-tuning adopts a cloze-style framework. In this strategy, prompt templates with masked slots are designed and concatenated with a relation mention. It is fed into a pre-trained language model to predict verbalize label categories. It is effective to decode the potential knowledge of a pre-trained language model ( \(\mathcal {M}\) ). In this framework, constructing appropriate prompts is important. At current, prompt templates are often manually designed. The process to concatenate prompt template with a given relation mention I can be denoted as:
\(\mathcal {T}(\cdot )\) denotes to a process to concatenate an appropriate prompt template T with I . \(\oplus \) denotes to the concatenate operation. The output \(I_{prompt}\) is a token sequence composed of the original sentence I and a prompt template T . The prompt template T is a special token sentence containing mask tokens “ [MASK] ”. An example of prompt-tuning for relation extraction is shown in the middle of Fig. 1 , where a relation instance I are mapped to \(I_{prompt}\) = “ [CLS] I . sergeant [MASK] car [SEP] ”. The special token [MASK] corresponds a set of answer words in verbalized label set \(\mathcal {V}\) . Then, \(I_{prompt}\) is fed into a pre-trained language model \(\mathcal {M}\) to learn the hidden representation \(\mathrm {H_{\texttt {[MASK]}}}\) of token [MASK] . The pre-trained language model \(\mathcal {M}\) predicts masked token as an answer word \(\mathcal {V}_I\) with the highest score:
In prompt-tuning, the process to extract entity relations is formalized as:
where \(\mathrm {H_{\texttt {[MASK]}}}\) refers to the hidden layer vectors of token [MASK] , \(\mathcal {V}_I\) means the corresponding answer word of a relation instance I . As shown in Fig. 1 b, predictions for the relation categories Entity - Origin ( e 1, e 2) and Entity - Destination ( e 1, e 2) can be expressed as:
To summarize, the process to extract relations in prompt-tuning is the same as the process to train a language model. It bridges the gap between pre-training objectives and fine-tuning objectives. It has the advantage to make use of potential knowledge in a pre-trained language model.
The methods flow of APRE. The yellow rectangles indicate special tokens [MASK] . In particular, the vanilla prompt also contains a [MASK] token. (Color figure online)
Our model, the Annotation-aware Prompt-tuning Model for Relation Extraction (APRE), aims to aggregate more semantic information from pre-trained language models for masked token prediction. Unlike manually designed prompt templates, our method implants entity annotations within a sentence to decode the semantic information of pre-trained language models, thereby improving the overall performance of our model. The architecture of our model to support annotation-aware prompt-tuning is given in Fig. 2 . This model is composed of three parts: annotation prompt construction, annotaion-aware module, and feature interaction mapping.
3.1 Annotation Construction
During the annotation construction phase, annotations are constructed for each entity. In previous studies, Zhou and Chen [ 20 ] leverage entity type information from prior knowledge as entity annotations to improve performance. Therefore, it is reasonable to assume that entities contain a wealth of knowledge of entity types. The pattern of entity annotation leverages the entity as the target and the context as the perceptual information, further mining the connections between them.
In our method, entity type tokens are adopted as entity annotations. They are implanted near named entities in the same sentence. Formally, given a relation instance \(I(e_s,e_o,r)\) , the constructor \( g (e_s)\) constructs entity annotations for each entity within the input. It is expressed as follows:
An example of the constructor \( g (e_s)\) is shown in the input part of Fig. 2 , “sergeant” is a named entity with entity type “soldier”. The entity type is implanted into the original sentence as “The sergeant (soldier) got out of the car”. Then, “soldier” serves as an annotation that reflects a connection to sergeant.
APRE can tune the PLMs and be aware of the context of [MASK] . We choose entity types based on prior knowledge as candidates \(\mathcal {V}_{e_{s}}\) for entity annotation prompt templates:
The structure of the annotation and the set of answer candidates for the corresponding prompt template for the objective entity \(e_o\) is similar to Eqs. ( 7 ) and ( 8 ).
We follow the vanilla prompts constructed by the previous prompt-tuning method [ 12 ] to restrict the relationship between entity pairs. The difference is that we have increased the verbalization of the vanilla prompt, making it closer to training text features and appropriately exploiting the MLM mechanism to mine potential connections between entity pairs. For instance, given an entity pair \((e_s,e_o)\) , we construct a vanilla prompt by constructor \(\textrm{g}(e_s,e_o)\) . Its corresponding answer template word \(W_{e_{s},e_{o}}\) can be expressed as:
For complex relationships of various entity pairs, a single prompt may not be enough to predict the target category. We adopt multiple annotation approach to construct prompts, leveraging the PLMs to learn the deeper semantic connections of the context between entity pairs. For the relation instance I , Table 1 shows the input of APRE after constructing annotation prompts.
3.2 Annotation Aware
In our model, a multi-head attention mechanism [ 25 ] is used in the annotation-aware module to obtain contextual semantic information of annotation prompts. Furthermore, we leverage annotation prompts to decrease the loss between the predictor and the answer word to optimize APRE.
Multi-head attention brings together different knowledge from multiple attention heads derived from different representation subspaces of Query ( Q ), Key ( K ), and Value ( V ). Through annotation prompts, multiple attention extracts characteristic information of a sentence from multiple dimensions. It is effective to prompt the acquisition of contextual semantics while paying attention to the special token [MASK] . The attention distribution \(\alpha _{\mathrm {\texttt {[MASK]}}}^{i}\) of ith [MASK] in relation instance I can be expressed as:
where \(q_{\mathrm {\texttt {[MASK]}}}^{i}\) denotes to the query of ith [MASK] . \(K^{T}\) represents the transpose of the matrix K and \(\sqrt{d_k}\) implies the scaling factor.
With ith single attention head, the attention \(head_i\) is represented as follow:
where \(Q_i=QW_i^Q\) , \(K_i=KW_i^K\) and \(V_i=VW_i^V\) . The projections \(W_i^Q\) , \(W_i^K\) and \(W_i^V\) are parameter matrices.
Subsequently, the output of the multi-head attention concatenate all heads and obtains the output through the dot product parameter matrice \(W_O\) :
Finally, the annotation aware module outputs the hidden layer vector through a fully connected feed-forward neural network.
After acquiring the output of the annotation-aware module, we need to predict the relation category based on representations of masked tokens [MASK] . Generally, prompt-tuning methods do not need an additional classifier. The training process is similar to the pre-training phase. However, in the case of prompt answer words, they are formally represented as the backbone of a sentence. Therefore, for a set of answer words, they have a non-negligible semantic connection. In order to further obtain the connection between them, when obtaining the prompt representation of the model output, we get the final semantic representation of the different position prompts through a feature interaction mapping, which will be discussed in following section.
3.3 Feature Interaction Mapping
To alleviate the prediction bias of PLMs on prompts, we leverage feature interaction mapping to obtain the final semantic representation of [MASK] in the prompts. In this module, we first concatenate three [MASK] token representations to construct the feature fusion vector \(\textrm{H}_{interact}\) :
where \(\mathrm {h_{\texttt {[MASK]}}^{i}}\) indicates the output of the ith special token [MASK] in the annotation aware module.
Then, we get the final representation \(\mathrm {H_{\texttt {[MASK]}}^{i}}\) of each special token [MASK] through three individual MLP:
Finally, we map the final output vectors of all special tokens [MASK] onto the corresponding relation category. Since annotation prompts are composed of multiple prompts, and each prompt is similar to a conditional function, our regular classification task is converted into the form of multi-condition functions. Meanwhile, as the multi-prompt template contains multiple [MASK] tokens, all the masked positions are applied to the prediction when we get the final output vectors. For example, the i th masked position corresponds to a set of answer words \(w \in \mathcal {V}\) . Given \(v \in w\) , we can calculate the probability distribution of the token v over w :
where v means the embedding of v in the pre-trained model and \(I_{prompt}\) refers to relation instance I with annotation prompts. For each relation instance, the probability distribution \(p(y\vert I)\) can be expressed as follows:
where n means the number of [MASK] token, w ( y ) indicates the answer words for the i th masked position of y . The answer words in annotation prompts are defined based on semantic knowledge of entities and relationships whose underlying spatial variables are associated with entities. Thus, we perform semantic optimization of the masked position through the semantic knowledge of entities. Furthermore, we mitigate the gap between PLMs and RE by calculating the cross-entropy loss of the prediction words \(p(y\vert I)\) and the answer words of relation category y :
where \(L_{\mathrm {\texttt {[MASK]}}}\) is the losses for [MASK] predictions. During the APRE training process, we enter the training data in batches. Ultimately, for each batch of data, the loss calculation and prediction are aimed at maximizing:
where \(\mathcal {X}\) represents the entire training collection. Masked positions in the prompt tuning can automatically sense entity information through the annotation-aware mechanism to learn the optimal representations of entity types.
4 Experiments
4.1 datasets and settings.
In this section, we conduct experiments on SemEval 2010 Task 8 (English) and analyse the results to demonstrate the effectiveness of our method. Meanwhile, our method is evaluated on the Chinese literature text corpus (Chinese) dataset to show the scalability of our proposed approach. The statistics of datasets as shown in Table 2 .
SemEval 2010 Task 8 (SemEval [ 26 ]) released on task 8 of the 2010 Semantic Evaluation Conference is a publicly available dataset in English. It contains 10,717 annotation examples divided into 8000 examples for training and 2717 examples for testing. Other than that, SemEval has a total of 18 relation types besides Other . In the dataset, all relations are directional. For example, Product-Producer(e1, e2) is different from Product-Producer(e2, e1).
Chinese literature text corpus (CLTC [ 27 ]) is a discourse-level corpus whose articles are made by manually labeling 837 Chinese essays. For facilitating comparison among different models, the dataset is divided into the training, validation, and test sets in advance. The corpus contains a total of 7 entity types and 9 relation types. Specially, there is no negative relation category in the CLTC dataset. In this experiment, our optimal performances are obtained on RoBERTa_WWM-LARGE (CLTC) and RoBERTa_LARGE (SemEval), respectively. We also experiment with the same PLMs to ensure the fairness of comparison. We follow the other hyper-parameter in the previous studies [ 7 , 12 , 28 ] and compare our model with related work: we optimize the model using the Adam optimizer with a learning rate of \(3e\!-\!5\) ;the value of warmup steps is 0.1; epochs and batch sizes are set to 20 and 16, respectively. Meanwhile, all of our experiments are performed on Nvidia A100 GPU. The F1 scores are used as the primary metric for evaluating the model. We select the checkpoint with the best validation performance to test during the tuning of the model. Most of other setup refers to the previous work [ 12 , 28 ]. The code to implement our model is available at: https://github.com/weichao21gzu/APRE .
4.2 Comparing with Other Models
In this section, our APRE model is compared to several related work. Based on the architecture of models, we roughly divide them into three strategies: Traditional neural networks, Fine-tuning methods, and Prompt-tuning methods. They are introduced as follows:
CR-CNN [ 29 ] considers only the text between the entity pair and uses it as input features for relation classification. CR-CNN achieves better performance without using any handcrafted features.
BRCNN [ 30 ] exploits dependencies on the shortest dependency path (SDP) by combining convolutional neural networks and long-short-term memory neural networks for RE. This approach leverages a bidirectional architecture to model the directional representation of relations.
SR-BRCNN [ 31 ] obtains the dependency relationship of entity pairs through building two bidirectional LSTMs on SDP. SR-BRCNN reduces the complexity of the overall model by learning relation representations by extracting the shortest dependency path, which facilitates the handling of text data with plenty of redundant information.
R-BERT [ 16 ] combines target entity information with contextualized word embeddings to perform relation extraction. This method utilizes a pre-trained architecture to transfer both sentence and entity information by identifying the target entities.
KnowBERT [ 17 ] retrieves entity embeddings by using entity links and updates the context representation between words and entities. This method enhances the knowledge representation of a model by building multiple knowledge bases of structured, human-curated knowledge, which are embedded into large models.
MTB [ 8 ] investigates ways to generate available representations of relationships directly from the original text. It constructs a task agnostic relation representation from the text of entity-linked based on extensions of Harris’ distributional hypothesis to relation.
RELA [ 32 ] RELA investigates the advantages of using sequence generation on RE, generating semantically equivalent synonyms for each relation name as the generation target, and exploring the impact of their textual semantics and correlations (word sequence patterns) on model performance. This method uses a generative model to complete the relation extraction task and provides an in-depth analysis of the Seq2Seq model’s ability to handle RE behavior.
KLG [ 33 ] explores whether the Top-k predicted set for a given sample contains useful information for predicting the correct label. The method effectively utilizes the Top-k predicted set to construct a label graph and examine the candidate labels in the set. Furthermore, the approach designs a dynamic k-selection mechanism that learns stronger and more discriminative representations for the relations.
SPOT [ 34 ] pretrains the model to learn representations of entities and relationships by using span and span-pair learning from the text during the pretraining phase. This approach represents the relations between entities by effectively encoding span-pair modules. In addition, this method utilizes a knowledge graph extracted from Wikipedia as an external knowledge base and applies it to the model’s pretraining.
PTR [ 12 ] constructs prompts with multiple sub-prompts based on logical rules and adapts prompt tuning with rules for relation classification. PTR encodes each class into prompt tuning through prior knowledge.
KnowPrompt [ 7 ] exploits a knowledge-aware approach with synergistic optimization in prompt tuning. Together, it optimizes the representation of the prompt of the virtual template and answers words under the constraints of knowledge.
BERT-CNN [ 4 ] implants structured entity indicators into the entity pair, which facilitates neural networks to encode syntax and learn semantic information. The entity indicators make the similar or identical internal symmetry of one sentence and entity pair more obvious.
EA-BERT [ 28 ] proposes a relation classification model based on the attention of entities for the Chinese literary text. It improves performance by filtering out redundant content and using the Attention Mechanism to extract critical information from the relation instances.
Our APRE model is compared with several typical methods for RE. It is mainly compared with other prompt tuning methods on SemEval. To verify the scalability of APRE on the Chinese dataset, we compare it with traditional neural networks and fine-tuning methods on CLTC. Since CR-CNN and BRCNN are the most efficient models based on traditional neural networks, we chose them as our baselines. The performance is shown in Tables 3 and 4 .
Traditional neural networks design complicated neural network architectures based on syntactic, lexical, and semantic features, which have achieved excellent performance effects. However, general neural network architectures have limitations in feature extraction. On the other hand, because English sentences usually have formal syntactic structures, traditional neural networks can benefit from syntactic knowledge of English sentences. However, Chinese sentences are not sensitive to sentence structures, it is difficult to use structured features in Chinese sentences. Therefore, the CLTC dataset has lower performance with traditional neural networks. The PLMs, which capture rich semantic knowledge from large-scale unlabeled data, exhibiting their powerful semantic representation. It achieves robust performance on two datasets. Its performance is superior to that of conventional methods in many downstream tasks.
Fine-tuning models leverage the plentiful semantic knowledge of PLMs, which can be used as a feature extractor that effortlessly obtain powerful representations of words or sentences. Further, external knowledge is encoded into PLMs, so that it can learn the representation of task-specific features. Fine-tuning can achieve better performance than conventional methods. In particular, the effect is evident in the CLTC dataset. BERT-CNN improves performance by 11.2% compared to SR-BRCNN. On the other hand, there is a mass of redundant information in Chinese literature. EA-BERT filters redundant content and utilizes an attention mechanism to extract critical information from relation instances. Its performance is approximately 9.1% better than BERT-CNN. Unlike simple fine-tuning methods, KnowBERT and MTB each employ different fine-tuning strategies and improve model performance. KnowBERT leverages knowledge base for data augmentation during the pre-training phase, enhancing the distributed representation of PLMs. Its performance is about 5.0% better than CR-CNN on SemEval. Besides, MTB enables the model to learn characteristic representations during the pre-training phase by injecting task-specific knowledge into the model parameters. The model is then fine-tuned without extra knowledge. The above results show that fine-tuning has more superiority over conventional methods. SPOT pre-trains the model to learn representations of entities and relationships by using span and span-pair learning from the text during the pretraining phase. This approach represents the relationships between entities by effectively encoding span-pair modules. In addition, this method utilizes a knowledge graph extracted from Wikipedia as an external knowledge base and applies it to the model’s pre-training. To sum up, PLMs serve a wide range of downstream tasks so that they are not highly pertinence. In particular, when the sample size of the fine-tuned data is small, the effect of fine-tuning is negligible.
Prompt-tuning models bridge the gap between specific tasks and PLMs. It alleviates the inability to fine-tune the model when there are fewer samples. PTR constructs prompts with multiple sub-prompts based on logical rules. Compared to the fine-tuning methods on SemEval, it has a slight performance improvement. KnowPrompt exploits a knowledge-aware approach with synergistic optimization, which has a 0.2% performance improvement over PTR on. In contrast, APRE is more pronounced in Chinese text than in fine-tuning methods. Due to the difference in pre-trained language models, their MLM are also different. Prompt tuning relies on MLM capabilities. Nevertheless, APRE performs better than current fine-tuning methods under the same conditions. We reproduced the PTR method on the CLTC dataset. APRE has improved performance by approximately 1.3% compared to this.
4.3 Performance on Few-Shot Learning
PLMs contain a large number pretrained parameters. They encode rich semantic knowledge, which is beneficial for few-shot learning. In this experiment, we evaluates the feasibility of our model for few-shot learning. We randomly collect K samples (K-shots, \(K \in [8, 16, 32]\) ) from each relation class in the training dataset. These samples are used to tune the PLMs. Then, the PLMs are evaluated on the entire test dataset. A fine-tuning and a prompt-tuning model (R-BERT and PTR) are conducted as baselines for comparison. The experimental results are listed in Table 5 .
The experimental results indicate that prompt-tuning is more suitable for few-shot learning than the compared to the fine-tuning methods. Because in fine-tuning models, several neural layers are stacked for extracting abstract features from the output of PLMs. It is not effective to optimize a fine-tunning model with very few samples. Therefore, the fine-tuning model achieves the lowest performance on all few-shot learning setting. On the other hand, the pre-training objective and fine-tuning objective are same in the prompt tuning. It is effective to make use of task-relevant features in PLMs. Comparing our model with the PTR model, our model steadly improves the performance in prompt-tuning. The result indicates that, benefited from the entity type representations, our annotation-aware prompt-tuning is more suitable for few-shot learning than the PTR methods.
As the number of samples are increased, the performance is steadily improved. The few-shot learning demonstrates impressive performance. It achieves competitive results at K = 32. Compared the performance between 16-shot and 32-shot, the performance of the R-BERT model has a large improvement, the result indicates that fine-tuning methods heavily depend on the amount of training data. The prompt tuning methods can quickly adjust PLMs with few training instances.
We also give the influence of training epochs. It is shown in Fig. 3 .
Influence of training epochs on few-shot learning using the SemEval dataset
It is shown in Fig. 3 , our model exhibits faster convergence speed and higher performance. It also displayed stable performance during training, which is consistent with the results in Table 5 . Compared to the R-BERT model, the prompt-tuning method still also has robust performance. Experimental results show that annotated prompts can better stimulate the potential of pre-trained language models than general prompts, thereby proving the feasibility of the proposed method.
4.4 Analysis
In Sect. 4.4.1 , we design different ablation strategies to conduct ablation experiments on entity annotation and vanilla prompts to demonstrate the validity of annotations. In Sect. 4.4.2 , we show the evaluation performance of each relationship category in the Sem dataset. The error analysis is given in Sect. 4.4.3 . At the same time, we provide visual analysis in 4.4.4 .
4.4.1 Ablation
Entity annotation means, after learning the contextual information, we only calculate the probability distribution of the [MASK] in the entity type candidates to map the relation category. The vanilla prompt indicates that we rely solely on predicting the connections between entities to map the corresponding relation. As shown in Table 6 , by screening for validity for each module of APRE, we can discover that performance improves with each additional module.
Due to the different types of entity pair for predefined relationship categories in the SemEval dataset and their directionality, single Entity annotation prompts are already capable of making precise predictions. Nonetheless, there are possibilities in the dataset where the entity types are nearly identical, but the corresponding relationships are diverse. Accordingly, the predicate relationship between entity pairs is particularly significant. From the above experimental results, It can be concluded that adding Vanilla prompt to Entity annotations improves precision by 1.13% and the overall F1 score increases by 0.73%. Since in the absence of the negative relation category, F1-micro score is equal to the scores of precision and recall.
4.4.2 Category Analysis
To further prove that annotations in APRE can be aware of contextual information and to verify its effectiveness and stability of APRE, we research on the performance of each category in each module of APRE. The result is shown in Table 7 .
Table 7 shows the performance of each category in different ablation strategies. Overall, the APRE is superior to other strategies in most categories. In particular, the vanilla prompt is unstable, with it excelling on Entity - Destination and Other and poorer on Message - Topic and Memory - Collection . In addition, the entity annotation is already equivalent to PTR on performance. On this basis, APRE alleviates the instability of the vanilla prompt and improves the performance of the entity annotation. For the learning of negative samples, APRE inherits the advantages of the vanilla prompt and improves the anti-interference resistance of the model during training.
4.4.3 Error Analysis
To verify the exactness of annotation awareness, during the evaluation phase, we compare the predictions of our method, PTR, and FINE-TUNING with several typical instances. The result is shown in Table 8 .
As shown in Table 8 , all three methods can judge the correct result in the first instance. For the second and third examples, FINE-TUNING is incorrectly labeled Cause - Effect ( e 2, e 1) and Instrument - Agency ( e 2, e 1), respectively. PTR mistakenly predicts the third example is Entity - Destination ( e 1, e 2). In contrast, APRE can leverage annotations to be aware of entities and contextual information, which is beneficial to labeling the relationship of the entity pair.
4.4.4 Visual Analysis
To further explore the ability of [MASK] in APRE to capture global and local semantics, we utilize BertViz [ 35 ] to visualize transformations from the attention of neurons. The result is shown in Fig. 4 .
View of neurons for APRE and vanilla prompt. Attention scores are determined based on line weight. The three plots from left to right represent views of neurons at different layers
As shown in Fig. 4 , most neurons pay more attention to their previous or latter neurons in the first layer (left). As the neural network layer deepens, [MASK] gradually captures local semantic information. The attention of each neuron begins to shift to [MASK] (middle). From the Figure (right), each [MASK] ultimately captures the attention of the majority of neurons, thereby enhancing their semantic representations. To elucidate, the attention of sergeant lies in its previous word while also focusing on the adjacent [MASK] . The above proves that entity annotations can effectively leverage entity information to rich the representation of [MASK] . Similarly, entities and [MASK] pay attention to each other in vanilla prompt.
5 Conclusion
In this paper, we propose an APRE model to encode entity annotation features for relation extraction. It is an annotation awareness prompt tuning model, which adopts entity annotation prompts to utilize semantic information of PLMs. Experimental results demonstrate that our approach achieves state-of-art performances, significantly outperforming current methods. Compared with other prompt tuning methods, APRE improves the performance on the SemEval dataset. We leverage annotation prompt tuning on the Chinese CLTC dataset, which contains a large amount of redundant data and is insensitive to structure. We have also achieved the state-of-the-art performance in the Chinese CLTC dataset. Furthermore, we offer a localization prompt design strategy that can be employed for prompt-based models. It also has the potential to be extended for other information extraction task. In further work, we will utilize APRE for cue calibration in semi-supervised or unsupervised relationship extraction. Finally, we’ll focus on automating relation extraction through annotation-aware prompt tuning.
Data Availability
The source code and preprocessed datasets utilized in this study are publicly accessible on Github: https://github.com/Weichao21gzu/APRE .
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Acknowledgements
This work is supported by the funds of the National Natural Science Foundation of China (No. 62166007, No. 62066007, No. 62066008), the funds of the Guizhou Provincial Science and Technology Projects (No. ZK[2022]027, No. ZK[2022]227) and the fund of Servyou Group (No. 2023610002000756). Thanks to the editors and anonymous reviewers for their valuable suggestions and comments that made our final version of the paper more perfect.
This work is supported by National Natural Science Foundation of China under Grant No. 62166007.
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CW was mainly responsible for the investigation, methodology, experiments, preparing figures 1–4, visual analysis and writing the main manuscript text. KW was responsible for formal analysis, and experimental verification. YQ supervised the research project and collected research resources. RH was responsible for supervising research projects and reviewing the manuscript. QZ contributed experimental guidance and visual analysis. YC was responsible for the conceptualization, guidance of research topics, methodology, review, and revision.
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Wei, C., Chen, Y., Wang, K. et al. APRE: Annotation-Aware Prompt-Tuning for Relation Extraction. Neural Process Lett 56 , 62 (2024). https://doi.org/10.1007/s11063-024-11437-y
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Task in a sentence up ( 0) down ( 0) Sentence count:198+64 Only show simple sentences Posted: 2017-05-06 Updated: 2020-07-24 Synonym: assignment , chore , duty , function , job , stint , work . Similar words: metastasis , Tass , taste , stash , tasty , potash , tassel , just as . Meaning: [tæsk /tɑːsk] n. 1.
Task is not a new verb. In fact, it has been verbing along since the 14th century, used with the meaning of "to assign a task to.". It also has an obsolete sense of "to impose a tax on," and an additional current meaning of "to oppress with great labor.". The word has shown an increase in use of late, particularly in business ...
A simple sentence contains a subject and a verb, and it may also have an object and modifiers. However, it contains only one independent clause. Key: Yellow, bold = subject; green underline = verb, blue, italics = object, pink, regular font =prepositional phrase. Here are a few examples: She wrote.
Depending on how you combine clauses, you can create four different types of sentence structure: Simple: 1 independent clause. Compound: 2 or more independent clauses. Complex:1 independent clause + 1 or more subordinate clauses. Compound-Complex: 2 or more independent clauses + 1 or more subordinate clauses.
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1 Declarative (statement): This is a standard sentence that points out a fact. Example: That dog won't sit. 2 Interrogative (question): This is a sentence asking a question. Example: Why won't that dog sit? 3 Exclamatory (exclamation): This is a modified declarative sentence used to add emphasis or show emotion, urgency, or high volume.
14 Sentences with Take To Task Examples. College professors may *take to task students who do not submit their assignments on time. *. If a student is caught cheating during an exam, the academic committee will certainly take them to task. Group projects require effective communication and teamwork; otherwise, the group leader may take to task ...
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Google has started rolling out "Help me write" — an experimental Gemini-powered generative AI feature for its Chrome browser that aims to help users write or refine text based on webpage ...
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OpenAI on Thursday teased its text-to-video artificial intelligence model Sora, which can generate videos up to a minute long based prompts users type into a text box.
Prompt-tuning has been successfully applied to support classification tasks in natural language processing and has achieved promising performance. The main characteristic of prompt-tuning based classification is to verbalize class labels and predict masked tokens like a cloze-like task. It has the advantage to make use of knowledge in pre-trained language models (PLMs). Because prompt ...
There's Normal, the default, which doesn't make any changes—you just get the default Windows 11 taskbar. Normal Credit: Justin Pot Next there's Opaque , which makes the taskbar a single color.