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        <title>Two Methods for Domain Adaptation of Bilingual Tasks: Delightfully Simple and Broadly Applicable</title>
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        <namePart type="given">Viktor</namePart>
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        <namePart type="given">Fabienne</namePart>
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            <title>Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)</title>
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            <namePart type="given">Iryna</namePart>
            <namePart type="family">Gurevych</namePart>
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            <namePart type="given">Yusuke</namePart>
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    <abstract>Bilingual tasks, such as bilingual lexicon induction and cross-lingual classification, are crucial for overcoming data sparsity in the target language. Resources required for such tasks are often out-of-domain, thus domain adaptation is an important problem here. We make two contributions. First, we test a delightfully simple method for domain adaptation of bilingual word embeddings. We evaluate these embeddings on two bilingual tasks involving different domains: cross-lingual twitter sentiment classification and medical bilingual lexicon induction. Second, we tailor a broadly applicable semi-supervised classification method from computer vision to these tasks. We show that this method also helps in low-resource setups. Using both methods together we achieve large improvements over our baselines, by using only additional unlabeled data.</abstract>
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%0 Conference Proceedings
%T Two Methods for Domain Adaptation of Bilingual Tasks: Delightfully Simple and Broadly Applicable
%A Hangya, Viktor
%A Braune, Fabienne
%A Fraser, Alexander
%A Schütze, Hinrich
%Y Gurevych, Iryna
%Y Miyao, Yusuke
%S Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F hangya-etal-2018-two
%X Bilingual tasks, such as bilingual lexicon induction and cross-lingual classification, are crucial for overcoming data sparsity in the target language. Resources required for such tasks are often out-of-domain, thus domain adaptation is an important problem here. We make two contributions. First, we test a delightfully simple method for domain adaptation of bilingual word embeddings. We evaluate these embeddings on two bilingual tasks involving different domains: cross-lingual twitter sentiment classification and medical bilingual lexicon induction. Second, we tailor a broadly applicable semi-supervised classification method from computer vision to these tasks. We show that this method also helps in low-resource setups. Using both methods together we achieve large improvements over our baselines, by using only additional unlabeled data.
%R 10.18653/v1/P18-1075
%U https://aclanthology.org/P18-1075/
%U https://doi.org/10.18653/v1/P18-1075
%P 810-820

Markdown (Informal)

[Two Methods for Domain Adaptation of Bilingual Tasks: Delightfully Simple and Broadly Applicable](https://aclanthology.org/P18-1075/) (Hangya et al., ACL 2018)

• Two Methods for Domain Adaptation of Bilingual Tasks: Delightfully Simple and Broadly Applicable (Hangya et al., ACL 2018)

ACL

• Viktor Hangya, Fabienne Braune, Alexander Fraser, and Hinrich Schütze. 2018. Two Methods for Domain Adaptation of Bilingual Tasks: Delightfully Simple and Broadly Applicable. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 810–820, Melbourne, Australia. Association for Computational Linguistics.