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        <title>End to End Multilingual Coreference Resolution for Indian Languages</title>
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        <namePart type="given">Sobha</namePart>
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        <namePart type="given">Vijay</namePart>
        <namePart type="given">Sundar</namePart>
        <namePart type="family">Ram</namePart>
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            <title>Proceedings of the 21st International Conference on Natural Language Processing (ICON)</title>
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            <namePart type="given">Karunesh</namePart>
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    <abstract>This paper describes an approach on an end to end model for Multilingual Coreference Resolution (CR) for low resource languages such as Tamil, Malayalam and Hindi. We have done fine tune the XLM-Roberta large model on multilingual training dataset using specific languages with linguistic features and without linguistic features. XLM-R with linguistic features achieves better results than the baseline system. This shows that giving the linguistic knowledge enriches the system performance. The performance of the system is comparable with the state of the art systems.</abstract>
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%0 Conference Proceedings
%T End to End Multilingual Coreference Resolution for Indian Languages
%A Lalitha Devi, Sobha
%A Ram, Vijay Sundar
%A RK Rao, Pattabhi
%Y Lalitha Devi, Sobha
%Y Arora, Karunesh
%S Proceedings of the 21st International Conference on Natural Language Processing (ICON)
%D 2024
%8 December
%I NLP Association of India (NLPAI)
%C AU-KBC Research Centre, Chennai, India
%F lalitha-devi-etal-2024-end
%X This paper describes an approach on an end to end model for Multilingual Coreference Resolution (CR) for low resource languages such as Tamil, Malayalam and Hindi. We have done fine tune the XLM-Roberta large model on multilingual training dataset using specific languages with linguistic features and without linguistic features. XLM-R with linguistic features achieves better results than the baseline system. This shows that giving the linguistic knowledge enriches the system performance. The performance of the system is comparable with the state of the art systems.
%U https://aclanthology.org/2024.icon-1.29/
%P 256-259

Markdown (Informal)

[End to End Multilingual Coreference Resolution for Indian Languages](https://aclanthology.org/2024.icon-1.29/) (Lalitha Devi et al., ICON 2024)

• End to End Multilingual Coreference Resolution for Indian Languages (Lalitha Devi et al., ICON 2024)

ACL

• Sobha Lalitha Devi, Vijay Sundar Ram, and Pattabhi RK Rao. 2024. End to End Multilingual Coreference Resolution for Indian Languages. In Proceedings of the 21st International Conference on Natural Language Processing (ICON), pages 256–259, AU-KBC Research Centre, Chennai, India. NLP Association of India (NLPAI).