{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/novel-positional-encodings-to-enable-tree","title":"Novel positional encodings to enable tree-based transformers","arxiv_id":null,"date":"2019-12-01","proceeding":"NeurIPS 2019 12","authors":["Vighnesh Shiv","Chris Quirk"],"abstract":"Neural models optimized for tree-based problems are of great value in tasks like SQL query extraction and program synthesis.\nOn sequence-structured data, transformers have been shown to learn relationships across arbitrary pairs of positions more reliably than recurrent models.\nMotivated 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.\nOur approach abstracts the transformer's sinusoidal positional encodings, allowing us to instead use a novel positional encoding scheme to represent node positions within trees.\nWe 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.\nIn particular, our results include a 22% absolute increase in accuracy on a JavaScript to CoffeeScript translation dataset.","url_abs":"http://papers.nips.cc/paper/9376-novel-positional-encodings-to-enable-tree-based-transformers","url_pdf":"http://papers.nips.cc/paper/9376-novel-positional-encodings-to-enable-tree-based-transformers.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"novel-positional-encodings-to-enable-tree","repo_url":"https://github.com/microsoft/icecaps","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"program-synthesis","task_name":"Program Synthesis"},{"task_slug":"semantic-parsing","task_name":"Semantic Parsing"},{"task_slug":"translation","task_name":"Translation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}