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    <titleInfo>
        <title>Cheap Translation for Cross-Lingual Named Entity Recognition</title>
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        <namePart type="given">Stephen</namePart>
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            <namePart type="given">Martha</namePart>
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    <abstract>Recent work in NLP has attempted to deal with low-resource languages but still assumed a resource level that is not present for most languages, e.g., the availability of Wikipedia in the target language. We propose a simple method for cross-lingual named entity recognition (NER) that works well in settings with very minimal resources. Our approach makes use of a lexicon to “translate” annotated data available in one or several high resource language(s) into the target language, and learns a standard monolingual NER model there. Further, when Wikipedia is available in the target language, our method can enhance Wikipedia based methods to yield state-of-the-art NER results; we evaluate on 7 diverse languages, improving the state-of-the-art by an average of 5.5% F1 points. With the minimal resources required, this is an extremely portable cross-lingual NER approach, as illustrated using a truly low-resource language, Uyghur.</abstract>
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%0 Conference Proceedings
%T Cheap Translation for Cross-Lingual Named Entity Recognition
%A Mayhew, Stephen
%A Tsai, Chen-Tse
%A Roth, Dan
%Y Palmer, Martha
%Y Hwa, Rebecca
%Y Riedel, Sebastian
%S Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F mayhew-etal-2017-cheap
%X Recent work in NLP has attempted to deal with low-resource languages but still assumed a resource level that is not present for most languages, e.g., the availability of Wikipedia in the target language. We propose a simple method for cross-lingual named entity recognition (NER) that works well in settings with very minimal resources. Our approach makes use of a lexicon to “translate” annotated data available in one or several high resource language(s) into the target language, and learns a standard monolingual NER model there. Further, when Wikipedia is available in the target language, our method can enhance Wikipedia based methods to yield state-of-the-art NER results; we evaluate on 7 diverse languages, improving the state-of-the-art by an average of 5.5% F1 points. With the minimal resources required, this is an extremely portable cross-lingual NER approach, as illustrated using a truly low-resource language, Uyghur.
%R 10.18653/v1/D17-1269
%U https://aclanthology.org/D17-1269
%U https://doi.org/10.18653/v1/D17-1269
%P 2536-2545

Markdown (Informal)

[Cheap Translation for Cross-Lingual Named Entity Recognition](https://aclanthology.org/D17-1269) (Mayhew et al., EMNLP 2017)

• Cheap Translation for Cross-Lingual Named Entity Recognition (Mayhew et al., EMNLP 2017)

ACL

• Stephen Mayhew, Chen-Tse Tsai, and Dan Roth. 2017. Cheap Translation for Cross-Lingual Named Entity Recognition. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 2536–2545, Copenhagen, Denmark. Association for Computational Linguistics.