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Chinese Event Discourse Deixis Resolution: Design of the Dataset and Model

Published: 20 November 2023 Publication History

Abstract

Anaphora resolution is a traditional task in the natural language processing community, defined as a cohesion phenomenon where one entity points back to a previous entity. Event discourse deixis (EDD) is a kind of more complex anaphora in which the anaphors refer to event descriptions such as sentences or clauses. Event discourse deixis resolution (EDDR) is able to help machines understand the richer linguistic and semantic information in the discourse. However, compared to anaphora resolution, EDDR has received relatively less research attention. In this work, we investigate the EDDR task by designing the corresponding dataset and model. First, we manually construct a high-quality Chinese corpus for EDDR, including 4,417 documents and 5,929 event chains that consist of event antecedents and anaphors. Second, we propose a deep neural network model for EDDR, which formulates the task into two subtasks, namely event anaphor recognition and event antecedent recognition. Our model is trained under the two subtasks jointly so that the EDDR task can be performed end to end. Besides our final model, we also build seven pipeline and joint models as baselines to build comprehensive benchmarks for follow-up research. Experimental results on our EDDR dataset show that our model outperforms all the baselines and achieves about 53%, 44%, 53%, and 63% F1s using standard anaphora resolution metrics such as CoNLL, MUC, B3, and Ceafe. The performances show that EDDR is a challenging task and worth researching in the future. Our dataset and model will be released to facilitate follow-up research.

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  1. Chinese Event Discourse Deixis Resolution: Design of the Dataset and Model

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    cover image ACM Transactions on Asian and Low-Resource Language Information Processing
    ACM Transactions on Asian and Low-Resource Language Information Processing  Volume 22, Issue 11
    November 2023
    255 pages
    ISSN:2375-4699
    EISSN:2375-4702
    DOI:10.1145/3633309
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    Publication History

    Published: 20 November 2023
    Online AM: 06 September 2023
    Accepted: 24 August 2023
    Revised: 13 June 2023
    Received: 14 July 2022
    Published in TALLIP Volume 22, Issue 11

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    Author Tags

    1. Discourse deixis resolution
    2. coreference resolution
    3. anaphora resolution
    4. event chain extraction

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    • National Key Research and Development Program of China
    • National Natural Science Foundation of China
    • Research Foundation of the Ministry of Education of China
    • Youth Fund for Humanities and Social Science Research of the Ministry of Education of China
    • General Project of the Natural Science Foundation of Hubei Province
    • Wuhan University
    • Fundamental Research Funds for the Central Universities

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