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        <title>Training Data Augmentation for Code-Mixed Translation</title>
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    <abstract>Machine translation of user-generated code-mixed inputs to English is of crucial importance in applications like web search and targeted advertising. We address the scarcity of parallel training data for training such models by designing a strategy of converting existing non-code-mixed parallel data sources to code-mixed parallel data. We present an m-BERT based procedure whose core learnable component is a ternary sequence labeling model, that can be trained with a limited code-mixed corpus alone. We show a 5.8 point increase in BLEU on heavily code-mixed sentences by training a translation model using our data augmentation strategy on an Hindi-English code-mixed translation task.</abstract>
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    <identifier type="doi">10.18653/v1/2021.naacl-main.459</identifier>
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%0 Conference Proceedings
%T Training Data Augmentation for Code-Mixed Translation
%A Gupta, Abhirut
%A Vavre, Aditya
%A Sarawagi, Sunita
%S Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
%D 2021
%8 June
%I Association for Computational Linguistics
%C Online
%F gupta-etal-2021-training
%X Machine translation of user-generated code-mixed inputs to English is of crucial importance in applications like web search and targeted advertising. We address the scarcity of parallel training data for training such models by designing a strategy of converting existing non-code-mixed parallel data sources to code-mixed parallel data. We present an m-BERT based procedure whose core learnable component is a ternary sequence labeling model, that can be trained with a limited code-mixed corpus alone. We show a 5.8 point increase in BLEU on heavily code-mixed sentences by training a translation model using our data augmentation strategy on an Hindi-English code-mixed translation task.
%R 10.18653/v1/2021.naacl-main.459
%U https://aclanthology.org/2021.naacl-main.459
%U https://doi.org/10.18653/v1/2021.naacl-main.459
%P 5760-5766

Markdown (Informal)

[Training Data Augmentation for Code-Mixed Translation](https://aclanthology.org/2021.naacl-main.459) (Gupta et al., NAACL 2021)

• Training Data Augmentation for Code-Mixed Translation (Gupta et al., NAACL 2021)

ACL

• Abhirut Gupta, Aditya Vavre, and Sunita Sarawagi. 2021. Training Data Augmentation for Code-Mixed Translation. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 5760–5766, Online. Association for Computational Linguistics.