{"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/abstract-syntax-networks-for-code-generation","title":"Abstract Syntax Networks for Code Generation and Semantic Parsing","arxiv_id":"1704.07535","date":"2017-04-25","proceeding":"ACL 2017 7","authors":["Maxim Rabinovich","Mitchell Stern","Dan Klein"],"abstract":"Tasks like code generation and semantic parsing require mapping unstructured\n(or partially structured) inputs to well-formed, executable outputs. We\nintroduce abstract syntax networks, a modeling framework for these problems.\nThe outputs are represented as abstract syntax trees (ASTs) and constructed by\na decoder with a dynamically-determined modular structure paralleling the\nstructure of the output tree. On the benchmark Hearthstone dataset for code\ngeneration, our model obtains 79.2 BLEU and 22.7% exact match accuracy,\ncompared to previous state-of-the-art values of 67.1 and 6.1%. Furthermore, we\nperform competitively on the Atis, Jobs, and Geo semantic parsing datasets with\nno task-specific engineering.","url_abs":"http://arxiv.org/abs/1704.07535v1","url_pdf":"http://arxiv.org/pdf/1704.07535v1.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":"abstract-syntax-networks-for-code-generation","repo_url":"https://github.com/xiye17/torchASN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"code-generation","task_name":"Code Generation"},{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"semantic-parsing","task_name":"Semantic Parsing"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/semantic-parsing-on-atis","task":"Semantic Parsing","dataset":"ATIS","model":"ASN (Rabinovich et al., 2017)","rank_in_archive_order":3,"of":4,"metrics":{"Accuracy":"85.3"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1704.07535","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}