Learning Semantic Correspondences with Less Supervision

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• worksheets.codalab.org/worksheets/0xd8ae7710960549868c4665d197ecc583
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Abstract

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.

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Reproductions