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        <title>Implicit Sense-labeled Connective Recognition as Text Generation</title>
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        <namePart type="given">Yui</namePart>
        <namePart type="family">Oka</namePart>
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            <title>Findings of the Association for Computational Linguistics: EMNLP 2023</title>
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            <namePart type="given">Houda</namePart>
            <namePart type="family">Bouamor</namePart>
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            <namePart type="given">Juan</namePart>
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        <name type="personal">
            <namePart type="given">Kalika</namePart>
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    <abstract>Implicit Discourse Relation Recognition (IDRR) involves identifying the sense label of an implicit connective between adjacent text spans. This has traditionally been approached as a classification task. However, some downstream tasks require more than just a sense label as well as the specific connective used. This paper presents Implicit Sense-labeled Connective Recognition (ISCR), which identifies the implicit connectives and their sense labels between adjacent text spans. ISCR can be treated as a classification task, but a large number of potential categories, sense labels, and uneven distribution of instances among them make this difficult. Instead, this paper handles the task as a text-generation task, using an encoder-decoder model to generate both connectives and their sense labels. Here, we explore a classification method and three kinds of text-generation methods. From our evaluation results on PDTB-3.0, we found that our method outperforms the conventional classification-based method.</abstract>
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    <identifier type="doi">10.18653/v1/2023.findings-emnlp.487</identifier>
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%0 Conference Proceedings
%T Implicit Sense-labeled Connective Recognition as Text Generation
%A Oka, Yui
%A Hirao, Tsutomu
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F oka-hirao-2023-implicit
%X Implicit Discourse Relation Recognition (IDRR) involves identifying the sense label of an implicit connective between adjacent text spans. This has traditionally been approached as a classification task. However, some downstream tasks require more than just a sense label as well as the specific connective used. This paper presents Implicit Sense-labeled Connective Recognition (ISCR), which identifies the implicit connectives and their sense labels between adjacent text spans. ISCR can be treated as a classification task, but a large number of potential categories, sense labels, and uneven distribution of instances among them make this difficult. Instead, this paper handles the task as a text-generation task, using an encoder-decoder model to generate both connectives and their sense labels. Here, we explore a classification method and three kinds of text-generation methods. From our evaluation results on PDTB-3.0, we found that our method outperforms the conventional classification-based method.
%R 10.18653/v1/2023.findings-emnlp.487
%U https://aclanthology.org/2023.findings-emnlp.487
%U https://doi.org/10.18653/v1/2023.findings-emnlp.487
%P 7307-7313

Markdown (Informal)

[Implicit Sense-labeled Connective Recognition as Text Generation](https://aclanthology.org/2023.findings-emnlp.487) (Oka & Hirao, Findings 2023)

• Implicit Sense-labeled Connective Recognition as Text Generation (Oka & Hirao, Findings 2023)

ACL

• Yui Oka and Tsutomu Hirao. 2023. Implicit Sense-labeled Connective Recognition as Text Generation. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 7307–7313, Singapore. Association for Computational Linguistics.