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  1. その他個人著作物
  2. 浅原 正幸

Dynamically Updating Event Representations for Temporal Relation Classification with Multi-category Learning

https://repository.ninjal.ac.jp/records/3283
https://repository.ninjal.ac.jp/records/3283
8017be6d-8b5a-46bd-afd8-7dab1ee1182f
名前 / ファイル ライセンス アクション
2020.findings-emnlp.121.pdf 2020.findings-emnlp.121.pdf (500.6 kB)
Item type 学術雑誌論文 / Journal Article(1)
公開日 2021-03-26
タイトル
言語 en
タイトル Dynamically Updating Event Representations for Temporal Relation Classification with Multi-category Learning
言語
言語 eng
資源タイプ
資源タイプ識別子 http://purl.org/coar/resource_type/c_6501
資源タイプ journal article
著者 Cheng, Fei

× Cheng, Fei

WEKO 10845

en Cheng, Fei

Search repository
Asahara, Masayuki

× Asahara, Masayuki

WEKO 10846

en Asahara, Masayuki

Search repository
Kobayashi, Ichiro

× Kobayashi, Ichiro

WEKO 10847

en Kobayashi, Ichiro

Search repository
Kurohashi, Sadao

× Kurohashi, Sadao

WEKO 10848

en Kurohashi, Sadao

Search repository
著者所属(英)
内容記述タイプ Other
内容記述 Kyoto University
著者所属(英)
内容記述タイプ Other
内容記述 National Institute for Japanese Language and Linguistics
著者所属(英)
内容記述タイプ Other
内容記述 Ochanomizu University
著者所属(英)
内容記述タイプ Other
内容記述 Kyoto University
抄録(英)
内容記述タイプ Other
内容記述 Temporal relation classification is a pair-wise task for identifying the relation of a temporal link (TLINK) 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 transfer learning baselines on both the English and Japanese data.
出版者
出版者 Association for Computational Linguistics
書誌情報 en : Findings of the Association for Computational Linguistics: EMNLP 2020

p. 1352-1357, 発行日 2020-11
DOI
関連タイプ isIdenticalTo
識別子タイプ DOI
関連識別子 10.18653/v1/2020.findings-emnlp.121
フォーマット
内容記述タイプ Other
内容記述 application/pdf
著者版フラグ
出版タイプ VoR
出版タイプResource http://purl.org/coar/version/c_970fb48d4fbd8a85
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