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Bootstrapping incremental dialogue systems from minimal data: the generalisation power of dialogue grammars
[article]
2017
arXiv
pre-print
We investigate an end-to-end method for automatically inducing task-based dialogue systems from small amounts of unannotated dialogue data. It combines an incremental semantic grammar - Dynamic Syntax and Type Theory with Records (DS-TTR) - with Reinforcement Learning (RL), where language generation and dialogue management are a joint decision problem. The systems thus produced are incremental: dialogues are processed word-by-word, shown previously to be essential in supporting natural,
arXiv:1709.07858v1
fatcat:idqxn2y7f5cwtd2i7hszdwn774