A semi supervised learning model for mapping sentences to logical forms with ambiguous supervision

Semantic parsing is the task of mapping a sentence in natural language to a meaning representation. The limitation of previous work on supervised semantic parsing is that it is very difficult to obtain annotated training data in which a sentence is paired with a semantic representation. To deal with this problem, we introduce a semi supervised learning model for semantic parsing with ambiguous supervision. The main idea of our method is to utilize a large amount of data, to enrich feature space with the maximum entropy model using our semantic learner. We evaluate the proposed models on standard corpora to demonstrate that our methods are suitable for semantic parsing. Experimental results show that the proposed methods work efficiently and well on ambiguous data and it is comparable to the state of the art methods.