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        <title>A Multi-task Model for Multilingual Trigger Detection and Classification</title>
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        <namePart type="given">Sovan</namePart>
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    <name type="personal">
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        <namePart type="family">Ekbal</namePart>
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    <name type="personal">
        <namePart type="given">Pushpak</namePart>
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            <title>Proceedings of the 16th International Conference on Natural Language Processing</title>
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            <namePart type="given">Dipti</namePart>
            <namePart type="given">Misra</namePart>
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    <abstract>In this paper we present a deep multi-task learning framework for multilingual event and argument trigger detection and classification. In our current work, we identify detection and classification of both event and argument triggers as related tasks and follow a multi-tasking approach to solve them simultaneously in contrast to the previous works where these tasks were solved separately or learning some of the above mentioned tasks jointly. We evaluate the proposed approach with multiple low-resource Indian languages. As there were no datasets available for the Indian languages, we have annotated disaster related news data crawled from the online news portal for different low-resource Indian languages for our experiments. Our empirical evaluation shows that multi-task model performs better than the single task model, and classification helps in trigger detection and vice-versa.</abstract>
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        <date>2019-12</date>
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%0 Conference Proceedings
%T A Multi-task Model for Multilingual Trigger Detection and Classification
%A Sahoo, Sovan Kumar
%A Saha, Saumajit
%A Ekbal, Asif
%A Bhattacharyya, Pushpak
%Y Sharma, Dipti Misra
%Y Bhattacharya, Pushpak
%S Proceedings of the 16th International Conference on Natural Language Processing
%D 2019
%8 December
%I NLP Association of India
%C International Institute of Information Technology, Hyderabad, India
%F sahoo-etal-2019-multi
%X In this paper we present a deep multi-task learning framework for multilingual event and argument trigger detection and classification. In our current work, we identify detection and classification of both event and argument triggers as related tasks and follow a multi-tasking approach to solve them simultaneously in contrast to the previous works where these tasks were solved separately or learning some of the above mentioned tasks jointly. We evaluate the proposed approach with multiple low-resource Indian languages. As there were no datasets available for the Indian languages, we have annotated disaster related news data crawled from the online news portal for different low-resource Indian languages for our experiments. Our empirical evaluation shows that multi-task model performs better than the single task model, and classification helps in trigger detection and vice-versa.
%U https://aclanthology.org/2019.icon-1.19
%P 160-169

Markdown (Informal)

[A Multi-task Model for Multilingual Trigger Detection and Classification](https://aclanthology.org/2019.icon-1.19) (Sahoo et al., ICON 2019)

• A Multi-task Model for Multilingual Trigger Detection and Classification (Sahoo et al., ICON 2019)

ACL

• Sovan Kumar Sahoo, Saumajit Saha, Asif Ekbal, and Pushpak Bhattacharyya. 2019. A Multi-task Model for Multilingual Trigger Detection and Classification. In Proceedings of the 16th International Conference on Natural Language Processing, pages 160–169, International Institute of Information Technology, Hyderabad, India. NLP Association of India.