Can You Traducir This? Machine Translation for Code-Switched Input

Jitao Xu; François Yvon

doi:10.18653/v1/2021.calcs-1.11

Can You Traducir This? Machine Translation for Code-Switched Input

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Abstract

Code-Switching (CSW) is a common phenomenon that occurs in multilingual geographic or social contexts, which raises challenging problems for natural language processing tools. We focus here on Machine Translation (MT) of CSW texts, where we aim to simultaneously disentangle and translate the two mixed languages. Due to the lack of actual translated CSW data, we generate artificial training data from regular parallel texts. Experiments show this training strategy yields MT systems that surpass multilingual systems for code-switched texts. These results are confirmed in an alternative task aimed at providing contextual translations for a L2 writing assistant.

Anthology ID:: 2021.calcs-1.11
Volume:: Proceedings of the Fifth Workshop on Computational Approaches to Linguistic Code-Switching
Month:: June
Year:: 2021
Address:: Online
Editors:: Thamar Solorio, Shuguang Chen, Alan W. Black, Mona Diab, Sunayana Sitaram, Victor Soto, Emre Yilmaz, Anirudh Srinivasan
Venue:: CALCS
SIG:
Publisher:: Association for Computational Linguistics
Note:
Pages:: 84–94
Language:
URL:: https://aclanthology.org/2021.calcs-1.11/
DOI:: 10.18653/v1/2021.calcs-1.11
Bibkey:
Cite (ACL):: Jitao Xu and François Yvon. 2021. Can You Traducir This? Machine Translation for Code-Switched Input. In Proceedings of the Fifth Workshop on Computational Approaches to Linguistic Code-Switching, pages 84–94, Online. Association for Computational Linguistics.
Cite (Informal):: Can You Traducir This? Machine Translation for Code-Switched Input (Xu & Yvon, CALCS 2021)
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PDF:: https://aclanthology.org/2021.calcs-1.11.pdf

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@inproceedings{xu-yvon-2021-traducir,
    title = "Can You Traducir This? Machine Translation for Code-Switched Input",
    author = "Xu, Jitao  and
      Yvon, Fran{\c{c}}ois",
    editor = "Solorio, Thamar  and
      Chen, Shuguang  and
      Black, Alan W.  and
      Diab, Mona  and
      Sitaram, Sunayana  and
      Soto, Victor  and
      Yilmaz, Emre  and
      Srinivasan, Anirudh",
    booktitle = "Proceedings of the Fifth Workshop on Computational Approaches to Linguistic Code-Switching",
    month = jun,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.calcs-1.11/",
    doi = "10.18653/v1/2021.calcs-1.11",
    pages = "84--94",
    abstract = "Code-Switching (CSW) is a common phenomenon that occurs in multilingual geographic or social contexts, which raises challenging problems for natural language processing tools. We focus here on Machine Translation (MT) of CSW texts, where we aim to simultaneously disentangle and translate the two mixed languages. Due to the lack of actual translated CSW data, we generate artificial training data from regular parallel texts. Experiments show this training strategy yields MT systems that surpass multilingual systems for code-switched texts. These results are confirmed in an alternative task aimed at providing contextual translations for a L2 writing assistant."
}

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%0 Conference Proceedings
%T Can You Traducir This? Machine Translation for Code-Switched Input
%A Xu, Jitao
%A Yvon, François
%Y Solorio, Thamar
%Y Chen, Shuguang
%Y Black, Alan W.
%Y Diab, Mona
%Y Sitaram, Sunayana
%Y Soto, Victor
%Y Yilmaz, Emre
%Y Srinivasan, Anirudh
%S Proceedings of the Fifth Workshop on Computational Approaches to Linguistic Code-Switching
%D 2021
%8 June
%I Association for Computational Linguistics
%C Online
%F xu-yvon-2021-traducir
%X Code-Switching (CSW) is a common phenomenon that occurs in multilingual geographic or social contexts, which raises challenging problems for natural language processing tools. We focus here on Machine Translation (MT) of CSW texts, where we aim to simultaneously disentangle and translate the two mixed languages. Due to the lack of actual translated CSW data, we generate artificial training data from regular parallel texts. Experiments show this training strategy yields MT systems that surpass multilingual systems for code-switched texts. These results are confirmed in an alternative task aimed at providing contextual translations for a L2 writing assistant.
%R 10.18653/v1/2021.calcs-1.11
%U https://aclanthology.org/2021.calcs-1.11/
%U https://doi.org/10.18653/v1/2021.calcs-1.11
%P 84-94

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Markdown (Informal)

[Can You Traducir This? Machine Translation for Code-Switched Input](https://aclanthology.org/2021.calcs-1.11/) (Xu & Yvon, CALCS 2021)

Can You Traducir This? Machine Translation for Code-Switched Input (Xu & Yvon, CALCS 2021)

ACL

Jitao Xu and François Yvon. 2021. Can You Traducir This? Machine Translation for Code-Switched Input. In Proceedings of the Fifth Workshop on Computational Approaches to Linguistic Code-Switching, pages 84–94, Online. Association for Computational Linguistics.