{"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,"footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13556,13545],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-740737","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-artificial-intelligence","msr-research-area-human-language-technologies","msr-locale-en_us"],"msr_publishername":"","msr_edition":"","msr_affiliation":"","msr_published_date":"2021-4","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"","msr_chapter":"","msr_isbn":"","msr_journal":"","msr_volume":"","msr_number":"","msr_editors":"","msr_series":"","msr_issue":"","msr_organization":"","msr_how_published":"","msr_notes":"","msr_highlight_text":"","msr_release_tracker_id":"","msr_original_fields_of_study":"","msr_download_urls":"","msr_external_url":"","msr_secondary_video_url":"","msr_longbiography":"","msr_microsoftintellectualproperty":1,"msr_main_download":"","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"file","viewUrl":"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2021\/04\/santy2021bertologicomix.pdf","id":"740740","title":"santy2021bertologicomix","label_id":"243109","label":0}],"msr_related_uploader":"","msr_attachments":[{"id":740740,"url":"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2021\/04\/santy2021bertologicomix.pdf"}],"msr-author-ordering":[{"type":"user_nicename","value":"Sebastin Santy","user_id":39895,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Sebastin Santy"},{"type":"text","value":"Anirudh Srinivasan","user_id":0,"rest_url":false},{"type":"user_nicename","value":"Monojit Choudhury","user_id":32996,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Monojit Choudhury"}],"msr_impact_theme":[],"msr_research_lab":[199562],"msr_event":[],"msr_group":[],"msr_project":[248216],"publication":[],"video":[],"download":[],"msr_publication_type":"inproceedings","_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item\/740737"}],"collection":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item"}],"about":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/types\/msr-research-item"}],"version-history":[{"count":1,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item\/740737\/revisions"}],"predecessor-version":[{"id":740743,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item\/740737\/revisions\/740743"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=740737"}],"wp:term":[{"taxonomy":"msr-content-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-content-type?post=740737"},{"taxonomy":"msr-research-highlight","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-highlight?post=740737"},{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=740737"},{"taxonomy":"msr-publication-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-publication-type?post=740737"},{"taxonomy":"msr-product-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-product-type?post=740737"},{"taxonomy":"msr-focus-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-focus-area?post=740737"},{"taxonomy":"msr-platform","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-platform?post=740737"},{"taxonomy":"msr-download-source","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-download-source?post=740737"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=740737"},{"taxonomy":"msr-field-of-study","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-field-of-study?post=740737"},{"taxonomy":"msr-conference","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-conference?post=740737"},{"taxonomy":"msr-journal","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-journal?post=740737"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=740737"},{"taxonomy":"msr-pillar","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-pillar?post=740737"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}