{"id":731434,"date":"2021-03-07T11:02:06","date_gmt":"2021-03-07T19:02:06","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&#038;p=731434"},"modified":"2021-03-07T11:02:06","modified_gmt":"2021-03-07T19:02:06","slug":"code-mixed-parse-trees-and-how-to-find-them","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/code-mixed-parse-trees-and-how-to-find-them\/","title":{"rendered":"Code-mixed parse trees and how to find them"},"content":{"rendered":"<p>In this paper, we explore the methods of obtaining parse trees of code-mixed sentences and analyse the obtained trees. Existing work has shown that linguistic theories can be used to generate code-mixed sentences from a set of parallel sentences. We build upon this work, using one of these theories, the Equivalence-Constraint theory to obtain the parse trees of synthetically generated code-mixed sentences and evaluate them with a neural constituency parser. We highlight the lack of a dataset non-synthetic code-mixed constituency parse trees and how it makes our evaluation difficult. To complete our evaluation, we convert a code-mixed dependency parse tree set into \u201cpseudo constituency trees\u201d and find that a parser trained on synthetically generated trees is able to decently parse these as well.<\/p>\n<div class=\"ms-editor-squiggler\" style=\"color: initial;font: initial;background: initial;background-blend-mode: initial;border: initial;border-radius: initial;border-collapse: initial;caption-side: initial;clear: initial;columns: initial;column-fill: initial;column-rule: initial;column-span: initial;cursor: initial;flex: initial;flex-flow: initial;float: initial;height: 0px;letter-spacing: initial;margin: initial;max-height: initial;max-width: initial;min-height: initial;min-width: initial;overflow: initial;padding: initial;text-align: initial;text-decoration: initial;text-indent: initial;text-transform: initial;vertical-align: initial;border-spacing: initial;width: initial\"><\/div>\n","protected":false},"excerpt":{"rendered":"<p>In this paper, we explore the methods of obtaining parse trees of code-mixed sentences and analyse the obtained trees. Existing work has shown that linguistic theories can be used to generate code-mixed sentences from a set of parallel sentences. We build upon this work, using one of these theories, the Equivalence-Constraint theory to obtain the [&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":[13545],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-field-of-study":[246694,246691,246808,252925,248686],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-731434","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-human-language-technologies","msr-locale-en_us","msr-field-of-study-artificial-intelligence","msr-field-of-study-computer-science","msr-field-of-study-natural-language-processing","msr-field-of-study-parse-tree","msr-field-of-study-parsing"],"msr_publishername":"European Language Resources 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Srinivasan","user_id":0,"rest_url":false},{"type":"text","value":"Sandipan Dandapat","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 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