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        <title>Dynamically Updating Event Representations for Temporal Relation Classification with Multi-category Learning</title>
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    <name type="personal">
        <namePart type="given">Fei</namePart>
        <namePart type="family">Cheng</namePart>
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    <name type="personal">
        <namePart type="given">Masayuki</namePart>
        <namePart type="family">Asahara</namePart>
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    <name type="personal">
        <namePart type="given">Ichiro</namePart>
        <namePart type="family">Kobayashi</namePart>
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    <name type="personal">
        <namePart type="given">Sadao</namePart>
        <namePart type="family">Kurohashi</namePart>
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            <title>Findings of the Association for Computational Linguistics: EMNLP 2020</title>
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            <namePart type="given">Trevor</namePart>
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            <namePart type="given">Yulan</namePart>
            <namePart type="family">He</namePart>
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        <name type="personal">
            <namePart type="given">Yang</namePart>
            <namePart type="family">Liu</namePart>
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    <abstract>Temporal relation classification is the pair-wise task for identifying the relation of a temporal link (TLINKs) between two mentions, i.e. event, time and document creation time (DCT). It leads to two crucial limits: 1) Two TLINKs involving a common mention do not share information. 2) Existing models with independent classifiers for each TLINK category (E2E, E2T and E2D) hinder from using the whole data. This paper presents an event centric model that allows to manage dynamic event representations across multiple TLINKs. Our model deals with three TLINK categories with multi-task learning to leverage the full size of data. The experimental results show that our proposal outperforms state-of-the-art models and two strong transfer learning baselines on both the English and Japanese data.</abstract>
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    <identifier type="doi">10.18653/v1/2020.findings-emnlp.121</identifier>
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        <url>https://aclanthology.org/2020.findings-emnlp.121</url>
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        <date>2020-11</date>
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            <start>1352</start>
            <end>1357</end>
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%0 Conference Proceedings
%T Dynamically Updating Event Representations for Temporal Relation Classification with Multi-category Learning
%A Cheng, Fei
%A Asahara, Masayuki
%A Kobayashi, Ichiro
%A Kurohashi, Sadao
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Findings of the Association for Computational Linguistics: EMNLP 2020
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F cheng-etal-2020-dynamically
%X Temporal relation classification is the pair-wise task for identifying the relation of a temporal link (TLINKs) between two mentions, i.e. event, time and document creation time (DCT). It leads to two crucial limits: 1) Two TLINKs involving a common mention do not share information. 2) Existing models with independent classifiers for each TLINK category (E2E, E2T and E2D) hinder from using the whole data. This paper presents an event centric model that allows to manage dynamic event representations across multiple TLINKs. Our model deals with three TLINK categories with multi-task learning to leverage the full size of data. The experimental results show that our proposal outperforms state-of-the-art models and two strong transfer learning baselines on both the English and Japanese data.
%R 10.18653/v1/2020.findings-emnlp.121
%U https://aclanthology.org/2020.findings-emnlp.121
%U https://doi.org/10.18653/v1/2020.findings-emnlp.121
%P 1352-1357

Markdown (Informal)

[Dynamically Updating Event Representations for Temporal Relation Classification with Multi-category Learning](https://aclanthology.org/2020.findings-emnlp.121) (Cheng et al., Findings 2020)

• Dynamically Updating Event Representations for Temporal Relation Classification with Multi-category Learning (Cheng et al., Findings 2020)

ACL

• Fei Cheng, Masayuki Asahara, Ichiro Kobayashi, and Sadao Kurohashi. 2020. Dynamically Updating Event Representations for Temporal Relation Classification with Multi-category Learning. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 1352–1357, Online. Association for Computational Linguistics.