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    <abstract>Models such as mBERT and XLMR have shown success in solving Code-Mixed NLP tasks even though they were not exposed to such text during pretraining. Code-Mixed NLP models have relied on using synthetically generated data along with naturally occurring data to improve their performance. Finetuning mBERT on such data improves it’s code-mixed performance, but the benefits of using the different types of Code-Mixed data aren’t clear. In this paper, we study the impact of finetuning with different types of code-mixed data and outline the changes that occur to the model during such finetuning. Our findings suggest that using naturally occurring code-mixed data brings in the best performance improvement after finetuning and that finetuning with any type of code-mixed text improves the responsivity of it’s attention heads to code-mixed text inputs.</abstract>
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%0 Conference Proceedings
%T BERTologiCoMix: How does Code-Mixing interact with Multilingual BERT?
%A Santy, Sebastin
%A Srinivasan, Anirudh
%A Choudhury, Monojit
%Y Ben-David, Eyal
%Y Cohen, Shay
%Y McDonald, Ryan
%Y Plank, Barbara
%Y Reichart, Roi
%Y Rotman, Guy
%Y Ziser, Yftah
%S Proceedings of the Second Workshop on Domain Adaptation for NLP
%D 2021
%8 April
%I Association for Computational Linguistics
%C Kyiv, Ukraine
%F santy-etal-2021-bertologicomix
%X Models such as mBERT and XLMR have shown success in solving Code-Mixed NLP tasks even though they were not exposed to such text during pretraining. Code-Mixed NLP models have relied on using synthetically generated data along with naturally occurring data to improve their performance. Finetuning mBERT on such data improves it’s code-mixed performance, but the benefits of using the different types of Code-Mixed data aren’t clear. In this paper, we study the impact of finetuning with different types of code-mixed data and outline the changes that occur to the model during such finetuning. Our findings suggest that using naturally occurring code-mixed data brings in the best performance improvement after finetuning and that finetuning with any type of code-mixed text improves the responsivity of it’s attention heads to code-mixed text inputs.
%U https://aclanthology.org/2021.adaptnlp-1.12
%P 111-121

Markdown (Informal)

[BERTologiCoMix: How does Code-Mixing interact with Multilingual BERT?](https://aclanthology.org/2021.adaptnlp-1.12) (Santy et al., AdaptNLP 2021)

• BERTologiCoMix: How does Code-Mixing interact with Multilingual BERT? (Santy et al., AdaptNLP 2021)

ACL

• Sebastin Santy, Anirudh Srinivasan, and Monojit Choudhury. 2021. BERTologiCoMix: How does Code-Mixing interact with Multilingual BERT?. In Proceedings of the Second Workshop on Domain Adaptation for NLP, pages 111–121, Kyiv, Ukraine. Association for Computational Linguistics.