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        <title>In-context Contrastive Learning for Event Causality Identification</title>
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    <name type="personal">
        <namePart type="given">Liang</namePart>
        <namePart type="family">Chao</namePart>
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    <name type="personal">
        <namePart type="given">Wei</namePart>
        <namePart type="family">Xiang</namePart>
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    <name type="personal">
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            <title>Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing</title>
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            <namePart type="given">Yaser</namePart>
            <namePart type="family">Al-Onaizan</namePart>
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        <name type="personal">
            <namePart type="given">Mohit</namePart>
            <namePart type="family">Bansal</namePart>
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            <namePart type="given">Yun-Nung</namePart>
            <namePart type="family">Chen</namePart>
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    <abstract>Event Causality Identification (ECI) aims at determining the existence of a causal relation between two events. Although recent prompt learning-based approaches have shown promising improvements on the ECI task, their performance are often subject to the delicate design of multiple prompts and the positive correlations between the main task and derivate tasks. The in-context learning paradigm provides explicit guidance for label prediction in the prompt learning paradigm, alleviating its reliance on complex prompts and derivative tasks. However, it does not distinguish between positive and negative demonstrations for analogy learning. Motivated from such considerations, this paper proposes an **I**n-**C**ontext **C**ontrastive **L**earning (ICCL) model that utilizes contrastive learning to enhance the effectiveness of both positive and negative demonstrations. Additionally, we apply contrastive learning to event pairs to better facilitate event causality identification. Our ICCL is evaluated on the widely used corpora, including the EventStoryLine and Causal-TimeBank, and results show significant performance improvements over the state-of-the-art algorithms.</abstract>
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    <identifier type="doi">10.18653/v1/2024.emnlp-main.51</identifier>
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        <url>https://aclanthology.org/2024.emnlp-main.51/</url>
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        <date>2024-11</date>
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            <start>868</start>
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%0 Conference Proceedings
%T In-context Contrastive Learning for Event Causality Identification
%A Chao, Liang
%A Xiang, Wei
%A Wang, Bang
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F chao-etal-2024-context
%X Event Causality Identification (ECI) aims at determining the existence of a causal relation between two events. Although recent prompt learning-based approaches have shown promising improvements on the ECI task, their performance are often subject to the delicate design of multiple prompts and the positive correlations between the main task and derivate tasks. The in-context learning paradigm provides explicit guidance for label prediction in the prompt learning paradigm, alleviating its reliance on complex prompts and derivative tasks. However, it does not distinguish between positive and negative demonstrations for analogy learning. Motivated from such considerations, this paper proposes an **I**n-**C**ontext **C**ontrastive **L**earning (ICCL) model that utilizes contrastive learning to enhance the effectiveness of both positive and negative demonstrations. Additionally, we apply contrastive learning to event pairs to better facilitate event causality identification. Our ICCL is evaluated on the widely used corpora, including the EventStoryLine and Causal-TimeBank, and results show significant performance improvements over the state-of-the-art algorithms.
%R 10.18653/v1/2024.emnlp-main.51
%U https://aclanthology.org/2024.emnlp-main.51/
%U https://doi.org/10.18653/v1/2024.emnlp-main.51
%P 868-881

Markdown (Informal)

[In-context Contrastive Learning for Event Causality Identification](https://aclanthology.org/2024.emnlp-main.51/) (Chao et al., EMNLP 2024)

• In-context Contrastive Learning for Event Causality Identification (Chao et al., EMNLP 2024)

ACL

• Liang Chao, Wei Xiang, and Bang Wang. 2024. In-context Contrastive Learning for Event Causality Identification. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 868–881, Miami, Florida, USA. Association for Computational Linguistics.