{"id":751528,"date":"2021-06-07T00:19:44","date_gmt":"2021-06-07T07:19:44","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-project&p=751528"},"modified":"2023-03-02T01:46:15","modified_gmt":"2023-03-02T09:46:15","slug":"retrack","status":"publish","type":"msr-project","link":"https:\/\/www.microsoft.com\/en-us\/research\/project\/retrack\/","title":{"rendered":"Modular and Efficient KBQA"},"content":{"rendered":"
Here we introduce ReTraCk<\/strong> (Retriever-Transducer-Checker), a neural semantic parsing framework for large scale knowledge base question answering (KBQA). ReTraCk is designed as a modular framework to achieve high flexibility and efficiency. The system includes a retriever to access relevant KB items efficiently, a transducer to generate logical form with syntax correctness guarantees, and a checker to improve the transduction procedure.<\/p>\n ReTraCk is ranked at top1 overall performance on the GrailQA leaderboard (opens in new tab)<\/span><\/a> (as of Apr\/2021) and obtains highly competitive performance on the classic WebQuestions Semantic Parse<\/a> benchmark. Users can interact with the system in a timely interactive manner, further demonstrating the efficiency of the proposed framework.<\/p>\n Source code and Data for a system demo at ACL 2021 are available on GitHub. (opens in new tab)<\/span><\/a> Contributions and collaboration are very welcome!<\/p>\n