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        <title>Language Modeling for Code-Mixing: The Role of Linguistic Theory based Synthetic Data</title>
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    <name type="personal">
        <namePart type="given">Adithya</namePart>
        <namePart type="family">Pratapa</namePart>
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    <name type="personal">
        <namePart type="given">Gayatri</namePart>
        <namePart type="family">Bhat</namePart>
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    <name type="personal">
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    <name type="personal">
        <namePart type="given">Sunayana</namePart>
        <namePart type="family">Sitaram</namePart>
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            <roleTerm authority="marcrelator" type="text">author</roleTerm>
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    <name type="personal">
        <namePart type="given">Sandipan</namePart>
        <namePart type="family">Dandapat</namePart>
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    <name type="personal">
        <namePart type="given">Kalika</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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        <name type="personal">
            <namePart type="given">Yusuke</namePart>
            <namePart type="family">Miyao</namePart>
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    <abstract>Training language models for Code-mixed (CM) language is known to be a difficult problem because of lack of data compounded by the increased confusability due to the presence of more than one language. We present a computational technique for creation of grammatically valid artificial CM data based on the Equivalence Constraint Theory. We show that when training examples are sampled appropriately from this synthetic data and presented in certain order (aka training curriculum) along with monolingual and real CM data, it can significantly reduce the perplexity of an RNN-based language model. We also show that randomly generated CM data does not help in decreasing the perplexity of the LMs.</abstract>
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    <identifier type="doi">10.18653/v1/P18-1143</identifier>
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        <date>2018-07</date>
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%0 Conference Proceedings
%T Language Modeling for Code-Mixing: The Role of Linguistic Theory based Synthetic Data
%A Pratapa, Adithya
%A Bhat, Gayatri
%A Choudhury, Monojit
%A Sitaram, Sunayana
%A Dandapat, Sandipan
%A Bali, Kalika
%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 pratapa-etal-2018-language
%X Training language models for Code-mixed (CM) language is known to be a difficult problem because of lack of data compounded by the increased confusability due to the presence of more than one language. We present a computational technique for creation of grammatically valid artificial CM data based on the Equivalence Constraint Theory. We show that when training examples are sampled appropriately from this synthetic data and presented in certain order (aka training curriculum) along with monolingual and real CM data, it can significantly reduce the perplexity of an RNN-based language model. We also show that randomly generated CM data does not help in decreasing the perplexity of the LMs.
%R 10.18653/v1/P18-1143
%U https://aclanthology.org/P18-1143
%U https://doi.org/10.18653/v1/P18-1143
%P 1543-1553

Markdown (Informal)

[Language Modeling for Code-Mixing: The Role of Linguistic Theory based Synthetic Data](https://aclanthology.org/P18-1143) (Pratapa et al., ACL 2018)

• Language Modeling for Code-Mixing: The Role of Linguistic Theory based Synthetic Data (Pratapa et al., ACL 2018)

ACL

• Adithya Pratapa, Gayatri Bhat, Monojit Choudhury, Sunayana Sitaram, Sandipan Dandapat, and Kalika Bali. 2018. Language Modeling for Code-Mixing: The Role of Linguistic Theory based Synthetic Data. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1543–1553, Melbourne, Australia. Association for Computational Linguistics.