Summary of Enhancing Complex Causality Extraction Via Improved Subtask Interaction and Knowledge Fusion, by Jinglong Gao et al.
Enhancing Complex Causality Extraction via Improved Subtask Interaction and Knowledge Fusion
by Jinglong Gao, Chen Lu, Xiao Ding, Zhongyang Li, Ting Liu, Bing Qin
First submitted to arxiv on: 6 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a unified Event Causality Extraction (ECE) framework called UniCE to address three key challenges in ECE: complex causality extraction, subtask interaction, and knowledge fusion. The proposed method designs a subtask interaction mechanism for mutual dependence between event extraction and causal relationship identification, as well as a knowledge fusion mechanism to integrate language models and structured knowledge graphs. Additionally, the framework employs separate decoders for each subtask to facilitate complex causality extraction. Experiments on three benchmark datasets demonstrate that UniCE achieves state-of-the-art performance, outperforming ChatGPT by at least 30% F1-score. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to find causal relationships between events in texts. The method, called UniCE, makes it easier to extract these relationships and also combines information from language models and structured knowledge graphs. It’s better than existing methods because it can handle complex situations where multiple causal relationships exist within a single sentence. The results show that this approach performs much better than ChatGPT. |
Keywords
» Artificial intelligence » F1 score
