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- W2116716943 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." @default.
- W2116716943 created "2016-06-24" @default.
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- W2116716943 date "2009-01-01" @default.
- W2116716943 modified "2023-09-26" @default.
- W2116716943 title "Learning semantic correspondences with less supervision" @default.
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- W2116716943 doi "https://doi.org/10.3115/1687878.1687893" @default.
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