Sciweavers

COLING
2010

Generative Alignment and Semantic Parsing for Learning from Ambiguous Supervision

12 years 10 months ago
Generative Alignment and Semantic Parsing for Learning from Ambiguous Supervision
We present a probabilistic generative model for learning semantic parsers from ambiguous supervision. Our approach learns from natural language sentences paired with world states consisting of multiple potential logical meaning representations. It disambiguates the meaning of each sentence while simultaneously learning a semantic parser that maps sentences into logical form. Compared to a previous generative model for semantic alignment, it also supports full semantic parsing. Experimental results on the Robocup sportscasting corpora in both English and Korean indicate that our approach produces more accurate semantic alignments than existing methods and also produces competitive semantic parsers and improved language generators.
Joohyun Kim, Raymond J. Mooney
Added 13 May 2011
Updated 13 May 2011
Type Journal
Year 2010
Where COLING
Authors Joohyun Kim, Raymond J. Mooney
Comments (0)