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Summary of Zero-shot Cross-lingual Document-level Event Causality Identification with Heterogeneous Graph Contrastive Transfer Learning, by Zhitao He et al.


Zero-Shot Cross-Lingual Document-Level Event Causality Identification with Heterogeneous Graph Contrastive Transfer Learning

by Zhitao He, Pengfei Cao, Zhuoran Jin, Yubo Chen, Kang Liu, Zhiqiang Zhang, Mengshu Sun, Jun Zhao

First submitted to arxiv on: 5 Mar 2024

Categories

  • Main: Computation and Language (cs.CL)
  • Secondary: Artificial Intelligence (cs.AI)

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GrooveSquid.com Paper Summaries

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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 novel approach to Event Causality Identification (ECI) for document-level ECI in low-resource languages using a heterogeneous graph interaction model with multi-granularity contrastive transfer learning (GIMC). The proposed framework introduces a graph interaction network to capture long-distance dependencies between events and a contrastive transfer learning module to align causal representations across languages. Experimental results show that GIMC outperforms previous state-of-the-art models by 9.4% and 8.2% in monolingual and multilingual scenarios, respectively, while also exceeding the performance of few-shot learning with GPT-3.5 by 24.3% in a multilingual scenario.
Low GrooveSquid.com (original content) Low Difficulty Summary
This paper is about helping computers understand how events are related to each other in documents written in different languages. Most research has focused on understanding individual sentences, but this paper looks at understanding entire documents instead. The researchers propose a new way of doing this using something called GIMC, which combines two main ideas: one that helps capture relationships between events and another that helps learn from one language to apply to others. They tested their approach and found it works better than other methods by a significant amount.

Keywords

* Artificial intelligence  * Few shot  * Gpt  * Transfer learning