Papers by Huanhuan Chen
Generation-Augmented and Embedding Fusion in Document-Level Event Argument Extraction (2025.coling-main)
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| Challenge: | Document-level event argument extraction is a crucial task that aims to extract arguments from the entire document, beyond sentence-level analysis. |
| Approach: | They propose a novel approach to document-level event argument extraction that integrates predefined templates and generative language models into a foundational embedding derived from a classification model. |
| Outcome: | The proposed approach is more effective than baseline models and data-efficient in low-resource scenarios. |
EX-FEVER: A Dataset for Multi-hop Explainable Fact Verification (2024.findings-acl)
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| Challenge: | Existing studies on fact verification lack a high-quality dataset for explainability . existing systems lack evidence retrieval and veracity prediction, limiting the ability to verify a claim . |
| Approach: | They propose a dataset for multi-hop explainable fact verification that summarises and modifies Wikipedia documents. |
| Outcome: | The proposed dataset aims to improve the accuracy of multi-hop explainable fact verification systems. |
Relation Classification with Entity Type Restriction (2021.findings-acl)
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| Challenge: | Existing methods regard all relations as candidate relations for the two entities, which leads to inappropriate relations being candidate relations. |
| Approach: | They propose a paradigm which exploits entity types to restrict candidate relations by mutual restrictions. |
| Outcome: | The proposed paradigm improves GCN and SpanBERT on a standard dataset by 6.9 and 4.4 F1 points. |
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. |
| Approach: | They propose to use Large Language Model (LLM) to do data augmentation and propose a bidirectional prompt template for prompt learning. |
| Outcome: | The proposed model outperforms the state-of-the-art on four datasets and outperformed existing methods on TACREV, RETACRED and Semeval. |