{"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/retrack-a-flexible-and-efficient-framework","title":"ReTraCk: A Flexible and Efficient Framework for Knowledge Base Question Answering","arxiv_id":null,"date":"2021-08-01","proceeding":"ACL 2021 5","authors":["Shuang Chen","Qian Liu","Zhiwei Yu","Chin-Yew Lin","Jian-Guang Lou","Feng Jiang"],"abstract":"We present Retriever-Transducer-Checker (ReTraCk), a neural semantic parsing framework for large scale knowledge base question answering (KBQA). ReTraCk is designed as a modular framework to maintain high flexibility. It includes a retriever to retrieve relevant KB items efficiently, a transducer to generate logical form with syntax correctness guarantees and a checker to improve transduction procedure. ReTraCk is ranked at top1 overall performance on the GrailQA leaderboard and obtains highly competitive performance on the typical WebQuestionsSP benchmark. Our system can interact with users timely, demonstrating the efficiency of the proposed framework.","url_abs":"https://aclanthology.org/2021.acl-demo.39","url_pdf":"https://aclanthology.org/2021.acl-demo.39.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":"retrack-a-flexible-and-efficient-framework","repo_url":"https://github.com/microsoft/KC/tree/main/papers/ReTraCk","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"knowledge-base-question-answering","task_name":"Knowledge Base Question Answering"},{"task_slug":"question-answering","task_name":"Question Answering"},{"task_slug":"semantic-parsing","task_name":"Semantic Parsing"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/knowledge-base-question-answering-on-grailqa","task":"Knowledge Base Question Answering","dataset":"GrailQA","model":"ReTraCk","rank_in_archive_order":1,"of":2,"metrics":{"Compositional EM":"61.5","Compositional F1":"70.9","I.I.D. EM":"84.4","I.I.D. F1":"87.5","Overall EM":"58.1","Overall F1":"65.3","Zero-shot EM":"44.6","Zero-shot F1":"52.5"},"uses_additional_data":false},{"leaderboard":"/sota/knowledge-base-question-answering-on-1","task":"Knowledge Base Question Answering","dataset":"WebQuestionsSP","model":"ReTraCk Oracle EL","rank_in_archive_order":6,"of":8,"metrics":{"F1":"74.7","Hits@1":"74.6"},"uses_additional_data":false},{"leaderboard":"/sota/knowledge-base-question-answering-on-1","task":"Knowledge Base Question Answering","dataset":"WebQuestionsSP","model":"ReTraCk","rank_in_archive_order":7,"of":8,"metrics":{"F1":"71","Hits@1":"71.6"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}