Papers with EE task
PESE: Event Structure Extraction using Pointer Network based Encoder-Decoder Architecture (2022.aacl-main)
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| Challenge: | Event extraction (EE) aims to find the events and event-related argument information from the text and represent them in a structured format. |
| Approach: | They propose to represent each event record in a unique tuple format that contains trigger phrase, trigger type, argument phrase, and corresponding role information. |
| Outcome: | The proposed model achieves competitive performance compared to the state-of-the-art methods. |
Emancipating Event Extraction from the Constraints of Long-Tailed Distribution Data Utilizing Large Language Models (2024.lrec-main)
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| Challenge: | Existing methods for EE depend on manual annotations, which are expensive and scarce. |
| Approach: | They propose to transform the event extraction task into multi-turn dialogues and a novel method for generating high-quality data. |
| Outcome: | The proposed methods significantly improve existing models’ performance with various paradigms and structures, especially on tail types. |
LC4EE: LLMs as Good Corrector for Event Extraction (2024.findings-acl)
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| Challenge: | Event extraction (EE) is a critical task in natural language processing, yet deploying a practical EE system remains challenging. |
| Approach: | They propose to leverage the superior extraction capability of LLMs and instruction-following ability of LRMs to construct a robust and highly available EE system. |
| Outcome: | The proposed method can identify and correct errors in SLMs predictions based on automatically generated feedback information and improve performance. |