{"id":740737,"date":"2021-04-16T04:48:08","date_gmt":"2021-04-16T11:48:08","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&#038;p=740737"},"modified":"2021-04-16T04:48:08","modified_gmt":"2021-04-16T11:48:08","slug":"bertologicomix-how-does-code-mixing-interact-with-multilingual-bert","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/bertologicomix-how-does-code-mixing-interact-with-multilingual-bert\/","title":{"rendered":"BERTologiCoMix: How does Code-Mixing interact with Multilingual BERT?"},"content":{"rendered":"<p><span dir=\"ltr\">Models such as mBERT and XLMR have <\/span><span dir=\"ltr\">shown success in solving Code-Mixed NLP <\/span><span dir=\"ltr\">tasks even though they were not exposed to <\/span><span dir=\"ltr\">such text during pretraining. <\/span><span dir=\"ltr\">Code-Mixed <\/span><span dir=\"ltr\">NLP models have relied on using synthetically <\/span><span dir=\"ltr\">generated data along with naturally occurring <\/span><span dir=\"ltr\">data to improve their performance. Finetun-<\/span><span dir=\"ltr\">ing<\/span><span dir=\"ltr\">1<\/span><span dir=\"ltr\">mBERT on such data improves it\u2019s code-<\/span><span dir=\"ltr\">mixed performance, but the benefits of using <\/span><span dir=\"ltr\">the different types of Code-Mixed data aren\u2019t <\/span><span dir=\"ltr\">clear. In this paper, we study the impact of fine-<\/span><span dir=\"ltr\">tuning with different types of code-mixed data <\/span><span dir=\"ltr\">and outline the changes that occur to the model <\/span><span dir=\"ltr\">during such finetuning. Our findings suggest <\/span><span dir=\"ltr\">that using naturally occurring code-mixed data <\/span><span dir=\"ltr\">brings in the best performance improvement <\/span><span dir=\"ltr\">after finetuning and that finetuning with any <\/span><span dir=\"ltr\">type of code-mixed text improves the respon<\/span><span dir=\"ltr\">sivity of it\u2019s attention heads to code-mixed text <\/span><span dir=\"ltr\">inputs<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>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. Finetun-ing1mBERT on such data improves it\u2019s code-mixed performance, but the benefits [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":null,"msr_publishername":"","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"","msr_number":"","msr_organization":"","msr_pages_string":"","msr_page_range_start":"","msr_page_range_end":"","msr_series":"","msr_volume":"","msr_copyright":"","msr_conference_name":"AdaptNLP EACL 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