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.
In REF , a generative model was used to learn the semantic correspondence between a text and world state represented as a set of records.
Learning Semantic Correspondences with Less Supervision
ACL-IJCNLP
Computer Science