Papers with ACE05 dataset
TISE: A Tripartite In-context Selection Method for Event Argument Extraction (2024.naacl-long)
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| Challenge: | Recent studies show that LLMs can finish inference by providing several examples. |
| Approach: | They propose a method which integrates three requirements when selecting an in-context example and integrates them into a set of determinantal point processes to enhance the reasoning capabilities of LLMs. |
| Outcome: | The proposed method can achieve superior performance with fewer examples and outperform some supervised methods. |
HySPA: Hybrid Span Generation for Scalable Text-to-Graph Extraction (2021.findings-acl)
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| Challenge: | Existing methods to extract information graphs are difficult to scale to datasets with longer input texts because of their secondorder space/time complexities. |
| Approach: | They propose a Hybrid SPan GenerAtor that invertibly maps the information graph to an alternating sequence of nodes and edge types and generates them via a hybrid span decoder. |
| Outcome: | The proposed method outperforms state-of-the-art methods on the ACE05 dataset. |
Zero-shot Event Detection Using a Textual Entailment Model as an Enhanced Annotator (2024.lrec-main)
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| Challenge: | Recent work proposed to use a pre-trained textual entailment model for event detection . but, those methods treated the TE model as a frozen annotator . |
| Approach: | They propose to use TE models to annotate large-scale unlabeled text and annotated data to fine-tune the TE model. |
| Outcome: | The proposed method outperforms baseline methods by 15% on the ACE05 dataset. |