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        <title>Neural End-to-End Coreference Resolution using Morphological Information</title>
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        <namePart type="given">Tuğba</namePart>
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            <title>Proceedings of the CRAC 2023 Shared Task on Multilingual Coreference Resolution</title>
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            <namePart type="given">Zdeněk</namePart>
            <namePart type="family">Žabokrtský</namePart>
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            <namePart type="given">Maciej</namePart>
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    <abstract>In morphologically rich languages, words consist of morphemes containing deeper information in morphology, and thus such languages may necessitate the use of morpheme-level representations as well as word representations. This study introduces a neural multilingual end-to-end coreference resolution system by incorporating morphological information in transformer-based word embeddings on the baseline model. This proposed model participated in the Sixth Workshop on Computational Models of Reference, Anaphora and Coreference (CRAC 2023). Including morphological information explicitly into the coreference resolution improves the performance, especially in morphologically rich languages (e.g., Catalan, Hungarian, and Turkish). The introduced model outperforms the baseline system by 2.57 percentage points on average by obtaining 59.53% CoNLL F-score.</abstract>
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            <start>34</start>
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%0 Conference Proceedings
%T Neural End-to-End Coreference Resolution using Morphological Information
%A Pamay Arslan, Tuğba
%A Acar, Kutay
%A Eryiğit, Gülşen
%Y Žabokrtský, Zdeněk
%Y Ogrodniczuk, Maciej
%S Proceedings of the CRAC 2023 Shared Task on Multilingual Coreference Resolution
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F pamay-arslan-etal-2023-neural
%X In morphologically rich languages, words consist of morphemes containing deeper information in morphology, and thus such languages may necessitate the use of morpheme-level representations as well as word representations. This study introduces a neural multilingual end-to-end coreference resolution system by incorporating morphological information in transformer-based word embeddings on the baseline model. This proposed model participated in the Sixth Workshop on Computational Models of Reference, Anaphora and Coreference (CRAC 2023). Including morphological information explicitly into the coreference resolution improves the performance, especially in morphologically rich languages (e.g., Catalan, Hungarian, and Turkish). The introduced model outperforms the baseline system by 2.57 percentage points on average by obtaining 59.53% CoNLL F-score.
%R 10.18653/v1/2023.crac-sharedtask.3
%U https://aclanthology.org/2023.crac-sharedtask.3
%U https://doi.org/10.18653/v1/2023.crac-sharedtask.3
%P 34-40

Markdown (Informal)

[Neural End-to-End Coreference Resolution using Morphological Information](https://aclanthology.org/2023.crac-sharedtask.3) (Pamay Arslan et al., CRAC-WS 2023)

• Neural End-to-End Coreference Resolution using Morphological Information (Pamay Arslan et al., CRAC-WS 2023)

ACL

• Tuğba Pamay Arslan, Kutay Acar, and Gülşen Eryiğit. 2023. Neural End-to-End Coreference Resolution using Morphological Information. In Proceedings of the CRAC 2023 Shared Task on Multilingual Coreference Resolution, pages 34–40, Singapore. Association for Computational Linguistics.