Challenge: Existing methods for relation extraction ignore semantics of relation labels . prompt-based fine-tuning has been proposed for RE .
Approach: They propose a method for relation extraction using prompt-based fine-tuning . they use auxiliary prompt-tuned learning task to make the model capture semantics of relation labels .
Outcome: The proposed method outperforms existing methods on four widely used RE benchmarks under fully supervised and low-resource settings.

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Challenge: Recent research shows that prompt-based learning improves performance on relation extraction tasks.
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Prompt Tuning for Few-shot Relation Extraction via Modeling Global and Local Graphs (2024.lrec-main)

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Challenge: Recent studies show that prompt-tuning is effective for few-shot relation extraction tasks.
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Generative Prompt Tuning for Relation Classification (2022.findings-emnlp)

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Challenge: Existing prompt tuning methods for RC are limited by label spaces and rigid prompt restrictions.
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Relation Classification via Bidirectional Prompt Learning with Data Augmentation by Large Language Model (2024.lrec-main)

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Challenge: Recent studies investigate Relation Extraction task from two different aspects.
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Knowledge-enhanced Prompt Tuning for Dialogue-based Relation Extraction with Trigger and Label Semantic (2024.lrec-main)

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Challenge: Existing methods to determine semantic relation between two arguments in dialogues are limited due to the low information density of text.
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RGL: A Simple yet Effective Relation Graph Augmented Prompt-based Tuning Approach for Few-Shot Learning (2022.findings-naacl)

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Challenge: Pre-trained language models (PLMs) are a good starting point for downstream applications, but it is difficult to generalize them to new tasks given a few labeled samples.
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GRASP: Guiding Model with RelAtional Semantics Using Prompt for Dialogue Relation Extraction (2022.coling-1)

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Challenge: Existing studies utilize pre-trained language models with extensive features to supplement the low information density of the dialogue by multiple speakers.
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PromptDA: Label-guided Data Augmentation for Prompt-based Few Shot Learners (2023.eacl-main)

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Challenge: Existing studies on prompt-based few-shot tuning focus on deriving proper label words with a verbalizer or generating prompt templates to elicit semantics from PLMs.
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Knowledgeable Prompt-tuning: Incorporating Knowledge into Prompt Verbalizer for Text Classification (2022.acl-long)

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Challenge: Recent studies suggest that pre-trained language models have gained rich knowledge during pre-training.
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Prompt Tuning for Discriminative Pre-trained Language Models (2022.findings-acl)

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Challenge: Recent studies have shown promising results of prompt tuning in stimulating pre-trained language models (PLMs) for natural language processing tasks.
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