Papers with beneficial tasks
Enhancing Dialogue-based Relation Extraction by Speaker and Trigger Words Prediction (2021.findings-acl)
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| Challenge: | Existing methods for identifying relations from dialogues do not fully consider the particularity of dialogues, making them difficult to understand the semantics between conversational arguments. |
| Approach: | They propose two tasks to enhance the extraction of dialogue-based relations . speaker prediction captures the characteristics of speakerrelated entities . the trigger words prediction provides supportive contexts for relations between arguments . |
| Outcome: | The proposed tasks improve the extraction of dialogue-based relations . speaker prediction captures the characteristics of speakerrelated entities . the trigger words prediction provides supportive contexts for relations between arguments . |
Learning to Predict Task Transferability via Soft Prompt (2023.emnlp-main)
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| Challenge: | Experimental results show that fine-tuning pretrained language models on helpful intermediate tasks yields further gains. |
| Approach: | They propose to train an affinity scoring function to predict transferability between tasks by conditioning on task embeddings. |
| Outcome: | The proposed method efficiently identifies beneficial tasks for transfer learning. |
What to Pre-Train on? Efficient Intermediate Task Selection (2021.emnlp-main)
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| Challenge: | Existing methods for fine-tuning intermediate tasks are inefficient and expensive. |
| Approach: | They propose to use a set of 42 intermediate and 11 target English classification, multiple choice, question answering, and sequence tagging tasks to identify the best settings for intermediate transfer learning. |
| Outcome: | The proposed methods achieve an average Regret@3 of 1% across all target tasks. |
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. |
| Approach: | They propose a Knowledge-Enhanced Prompt-Tuning method to enhance DRE model by exploiting trigger and label semantics. |
| Outcome: | The proposed method achieves state-of-the-art in F1 and F1c scores on a DialogRE dataset. |