Learning Semantic Correspondences with Less Supervision

A central problem in grounded language acquisition is learning the correspondences between a rich world state and a stream of text which references that world state. To deal with the high degree of ambiguity present in this setting, we present a generative model that simultaneously segments the text into utterances and maps each utterance to a meaning representation grounded in the world state. We show that our model generalizes across three domains of increasing difficulty--Robocup sportscasting, weather forecasts (a new domain), and NFL recaps.

• Type: Journal
• Year: 2009
• Where: ACL
• Authors: Percy Liang, Michael I. Jordan, Dan Klein
• Keywords: ACL 2009, Computational Linguistics, Language Acquisition, Rich World State, World State