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    <titleInfo>
        <title>Learning to Respond to Mixed-code Queries using Bilingual Word Embeddings</title>
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    <name type="personal">
        <namePart type="given">Chia-Fang</namePart>
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    <name type="personal">
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    <name type="personal">
        <namePart type="given">Chingyu</namePart>
        <namePart type="family">Yang</namePart>
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            <namePart type="given">Annie</namePart>
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    <abstract>We present a method for learning bilingual word embeddings in order to support second language (L2) learners in finding recurring phrases and example sentences that match mixed-code queries (e.g., “接 受 sentence”) composed of words in both target language and native language (L1). In our approach, mixed-code queries are transformed into target language queries aimed at maximizing the probability of retrieving relevant target language phrases and sentences. The method involves converting a given parallel corpus into mixed-code data, generating word embeddings from mixed-code data, and expanding queries in target languages based on bilingual word embeddings. We present a prototype search engine, x.Linggle, that applies the method to a linguistic search engine for a parallel corpus. Preliminary evaluation on a list of common word-translation shows that the method performs reasonablly well.</abstract>
    <identifier type="citekey">ho-etal-2019-learning</identifier>
    <identifier type="doi">10.18653/v1/N19-4005</identifier>
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        <date>2019-06</date>
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%0 Conference Proceedings
%T Learning to Respond to Mixed-code Queries using Bilingual Word Embeddings
%A Ho, Chia-Fang
%A Chang, Jason
%A Chen, Jhih-Jie
%A Yang, Chingyu
%Y Ammar, Waleed
%Y Louis, Annie
%Y Mostafazadeh, Nasrin
%S Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics (Demonstrations)
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F ho-etal-2019-learning
%X We present a method for learning bilingual word embeddings in order to support second language (L2) learners in finding recurring phrases and example sentences that match mixed-code queries (e.g., “接 受 sentence”) composed of words in both target language and native language (L1). In our approach, mixed-code queries are transformed into target language queries aimed at maximizing the probability of retrieving relevant target language phrases and sentences. The method involves converting a given parallel corpus into mixed-code data, generating word embeddings from mixed-code data, and expanding queries in target languages based on bilingual word embeddings. We present a prototype search engine, x.Linggle, that applies the method to a linguistic search engine for a parallel corpus. Preliminary evaluation on a list of common word-translation shows that the method performs reasonablly well.
%R 10.18653/v1/N19-4005
%U https://aclanthology.org/N19-4005
%U https://doi.org/10.18653/v1/N19-4005
%P 24-28

Markdown (Informal)

[Learning to Respond to Mixed-code Queries using Bilingual Word Embeddings](https://aclanthology.org/N19-4005) (Ho et al., NAACL 2019)

• Learning to Respond to Mixed-code Queries using Bilingual Word Embeddings (Ho et al., NAACL 2019)

ACL

• Chia-Fang Ho, Jason Chang, Jhih-Jie Chen, and Chingyu Yang. 2019. Learning to Respond to Mixed-code Queries using Bilingual Word Embeddings. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics (Demonstrations), pages 24–28, Minneapolis, Minnesota. Association for Computational Linguistics.