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        <title>Code-switched Language Models Using Dual RNNs and Same-Source Pretraining</title>
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        <namePart type="given">Saurabh</namePart>
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        <namePart type="given">Preethi</namePart>
        <namePart type="family">Jyothi</namePart>
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            <title>Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing</title>
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        <name type="personal">
            <namePart type="given">David</namePart>
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            <namePart type="given">Jun’ichi</namePart>
            <namePart type="family">Tsujii</namePart>
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    <abstract>This work focuses on building language models (LMs) for code-switched text. We propose two techniques that significantly improve these LMs: 1) A novel recurrent neural network unit with dual components that focus on each language in the code-switched text separately 2) Pretraining the LM using synthetic text from a generative model estimated using the training data. We demonstrate the effectiveness of our proposed techniques by reporting perplexities on a Mandarin-English task and derive significant reductions in perplexity.</abstract>
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    <identifier type="doi">10.18653/v1/D18-1346</identifier>
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%0 Conference Proceedings
%T Code-switched Language Models Using Dual RNNs and Same-Source Pretraining
%A Garg, Saurabh
%A Parekh, Tanmay
%A Jyothi, Preethi
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F garg-etal-2018-code
%X This work focuses on building language models (LMs) for code-switched text. We propose two techniques that significantly improve these LMs: 1) A novel recurrent neural network unit with dual components that focus on each language in the code-switched text separately 2) Pretraining the LM using synthetic text from a generative model estimated using the training data. We demonstrate the effectiveness of our proposed techniques by reporting perplexities on a Mandarin-English task and derive significant reductions in perplexity.
%R 10.18653/v1/D18-1346
%U https://aclanthology.org/D18-1346
%U https://doi.org/10.18653/v1/D18-1346
%P 3078-3083

Markdown (Informal)

[Code-switched Language Models Using Dual RNNs and Same-Source Pretraining](https://aclanthology.org/D18-1346) (Garg et al., EMNLP 2018)

• Code-switched Language Models Using Dual RNNs and Same-Source Pretraining (Garg et al., EMNLP 2018)

ACL

• Saurabh Garg, Tanmay Parekh, and Preethi Jyothi. 2018. Code-switched Language Models Using Dual RNNs and Same-Source Pretraining. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 3078–3083, Brussels, Belgium. Association for Computational Linguistics.