Paper: Grounded Unsupervised Semantic Parsing

ACL ID P13-1092
Title Grounded Unsupervised Semantic Parsing
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2013
Authors

We present the first unsupervised ap- proach for semantic parsing that rivals the accuracy of supervised approaches in translating natural-language questions to database queries. Our GUSP system produces a semantic parse by annotat- ing the dependency-tree nodes and edges with latent states, and learns a proba- bilistic grammar using EM. To compen- sate for the lack of example annotations or question-answer pairs, GUSP adopts a novel grounded-learning approach to leverage database for indirect supervision. On the challenging ATIS dataset, GUSP attained an accuracy of 84%, effectively tying with the best published results by su- pervised approaches.