Dialogue Response Generation Using Completion of Omitted Predicate Arguments Based on Zero Anaphora Resolution
Ayaka Ueyama,
Yoshinobu Kano
Abstract:Human conversation attempts to build common ground consisting of shared beliefs, knowledge, and perceptions that form the premise for understanding utterances. Recent deep learningbased dialogue systems use human dialogue data to train a mapping from a dialogue history to responses, but common ground not directly expressed in words makes it difficult to generate coherent responses by learning statistical patterns alone. We propose Dialogue Completion using Zero Anaphora Resolution (DCZAR), a framework that exp… Show more
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