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        <title>Leveraging Multilingual Training for Limited Resource Event Extraction</title>
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        <namePart type="given">Andrew</namePart>
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        <namePart type="given">Yiming</namePart>
        <namePart type="family">Yang</namePart>
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    <name type="personal">
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    <name type="personal">
        <namePart type="given">Ruochen</namePart>
        <namePart type="family">Xu</namePart>
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            <title>Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers</title>
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            <namePart type="given">Yuji</namePart>
            <namePart type="family">Matsumoto</namePart>
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            <namePart type="given">Rashmi</namePart>
            <namePart type="family">Prasad</namePart>
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    <abstract>Event extraction has become one of the most important topics in information extraction, but to date, there is very limited work on leveraging cross-lingual training to boost performance. We propose a new event extraction approach that trains on multiple languages using a combination of both language-dependent and language-independent features, with particular focus on the case where target domain training data is of very limited size. We show empirically that multilingual training can boost performance for the tasks of event trigger extraction and event argument extraction on the Chinese ACE 2005 dataset.</abstract>
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%0 Conference Proceedings
%T Leveraging Multilingual Training for Limited Resource Event Extraction
%A Hsi, Andrew
%A Yang, Yiming
%A Carbonell, Jaime
%A Xu, Ruochen
%Y Matsumoto, Yuji
%Y Prasad, Rashmi
%S Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F hsi-etal-2016-leveraging
%X Event extraction has become one of the most important topics in information extraction, but to date, there is very limited work on leveraging cross-lingual training to boost performance. We propose a new event extraction approach that trains on multiple languages using a combination of both language-dependent and language-independent features, with particular focus on the case where target domain training data is of very limited size. We show empirically that multilingual training can boost performance for the tasks of event trigger extraction and event argument extraction on the Chinese ACE 2005 dataset.
%U https://aclanthology.org/C16-1114
%P 1201-1210

Markdown (Informal)

[Leveraging Multilingual Training for Limited Resource Event Extraction](https://aclanthology.org/C16-1114) (Hsi et al., COLING 2016)

• Leveraging Multilingual Training for Limited Resource Event Extraction (Hsi et al., COLING 2016)

ACL

• Andrew Hsi, Yiming Yang, Jaime Carbonell, and Ruochen Xu. 2016. Leveraging Multilingual Training for Limited Resource Event Extraction. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 1201–1210, Osaka, Japan. The COLING 2016 Organizing Committee.