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Bootstrapping incremental dialogue systems from minimal data: the generalisation power of dialogue grammars
2017
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
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,
doi:10.18653/v1/d17-1236
dblp:conf/emnlp/EshghiSL17
fatcat:qi3ncbweqffbjhz4uypgkiirxq