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        <title>Low-resource Cross-lingual Event Type Detection via Distant Supervision with Minimal Effort</title>
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        <namePart type="given">Aldrian</namePart>
        <namePart type="given">Obaja</namePart>
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    <name type="personal">
        <namePart type="given">Ruochen</namePart>
        <namePart type="family">Xu</namePart>
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    <name type="personal">
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    <name type="personal">
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            <title>Proceedings of the 27th International Conference on Computational Linguistics</title>
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    <abstract>The use of machine learning for NLP generally requires resources for training. Tasks performed in a low-resource language usually rely on labeled data in another, typically resource-rich, language. However, there might not be enough labeled data even in a resource-rich language such as English. In such cases, one approach is to use a hand-crafted approach that utilizes only a small bilingual dictionary with minimal manual verification to create distantly supervised data. Another is to explore typical machine learning techniques, for example adversarial training of bilingual word representations. We find that in event-type detection task—the task to classify [parts of] documents into a fixed set of labels—they give about the same performance. We explore ways in which the two methods can be complementary and also see how to best utilize a limited budget for manual annotation to maximize performance gain.</abstract>
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        <url>https://aclanthology.org/C18-1007</url>
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        <date>2018-aug</date>
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%0 Conference Proceedings
%T Low-resource Cross-lingual Event Type Detection via Distant Supervision with Minimal Effort
%A Muis, Aldrian Obaja
%A Otani, Naoki
%A Vyas, Nidhi
%A Xu, Ruochen
%A Yang, Yiming
%A Mitamura, Teruko
%A Hovy, Eduard
%S Proceedings of the 27th International Conference on Computational Linguistics
%D 2018
%8 aug
%I Association for Computational Linguistics
%C Santa Fe, New Mexico, USA
%F muis-etal-2018-low
%X The use of machine learning for NLP generally requires resources for training. Tasks performed in a low-resource language usually rely on labeled data in another, typically resource-rich, language. However, there might not be enough labeled data even in a resource-rich language such as English. In such cases, one approach is to use a hand-crafted approach that utilizes only a small bilingual dictionary with minimal manual verification to create distantly supervised data. Another is to explore typical machine learning techniques, for example adversarial training of bilingual word representations. We find that in event-type detection task—the task to classify [parts of] documents into a fixed set of labels—they give about the same performance. We explore ways in which the two methods can be complementary and also see how to best utilize a limited budget for manual annotation to maximize performance gain.
%U https://aclanthology.org/C18-1007
%P 70-82

Markdown (Informal)

[Low-resource Cross-lingual Event Type Detection via Distant Supervision with Minimal Effort](https://aclanthology.org/C18-1007) (Muis et al., COLING 2018)

• Low-resource Cross-lingual Event Type Detection via Distant Supervision with Minimal Effort (Muis et al., COLING 2018)

ACL

• Aldrian Obaja Muis, Naoki Otani, Nidhi Vyas, Ruochen Xu, Yiming Yang, Teruko Mitamura, and Eduard Hovy. 2018. Low-resource Cross-lingual Event Type Detection via Distant Supervision with Minimal Effort. In Proceedings of the 27th International Conference on Computational Linguistics, pages 70–82, Santa Fe, New Mexico, USA. Association for Computational Linguistics.