{"id":617847,"date":"2019-10-27T19:48:16","date_gmt":"2019-10-28T02:48:16","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&#038;p=617847"},"modified":"2019-12-09T16:57:24","modified_gmt":"2019-12-10T00:57:24","slug":"novel-positional-encodings-to-enable-tree-based-transformers","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/novel-positional-encodings-to-enable-tree-based-transformers\/","title":{"rendered":"Novel positional encodings to enable tree-based transformers"},"content":{"rendered":"<p>Neural models optimized for tree-based problems are of great value in tasks like SQL query extraction and program synthesis. On sequence-structured data, transformers have been shown to learn relationships across arbitrary pairs of positions more reliably than recurrent models. Motivated by this property, we propose a method to extend transformers to tree-structured data, enabling sequence-to-tree, tree-to-sequence, and tree-to-tree mappings. Our approach abstracts the transformer&#8217;s sinusoidal positional encodings, allowing us to instead use a novel positional encoding scheme to represent node positions within trees. We evaluated our model in tree-to-tree program translation and sequence-to-tree semantic parsing settings, achieving superior performance over both sequence-to-sequence transformers and state-of-the-art tree-based LSTMs on several datasets.\u00a0 In particular, our results include a 22% absolute increase in accuracy on a JavaScript to CoffeeScript translation dataset.<\/p>\n<p><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2019\/10\/NeurIPS-2019-poster.pdf\">Poster as PDF<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Neural models optimized for tree-based problems are of great value in tasks like SQL query extraction and program synthesis. On sequence-structured data, transformers have been shown to learn relationships across arbitrary pairs of positions more reliably than recurrent models. Motivated by this property, we propose a method to extend transformers to tree-structured data, enabling sequence-to-tree, [&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":[{"type":"user_nicename","value":"Vighnesh Leonardo Shiv","user_id":"38166"},{"type":"user_nicename","value":"Chris Quirk","user_id":"31430"}],"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":"NeurIPS 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