Abstract
Spoken language generation for dialogue systems requires a dictionary of mappings between the semantic representations of concepts that the system wants to express and the realizations of those concepts. Dictionary creation is a costly process; it is currently done by hand for each dialogue domain. We propose a novel unsupervised method for learning such mappings from user reviews in the target domain and test it in the restaurant and hotel domains. Experimental results show that the acquired mappings achieve high consistency between the semantic representation and the realization and that the naturalness of the realization is significantly higher than the baseline.
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