Deep Reinforcement Learning for Inquiry Dialog Policies with Logical Formula Embeddings

Autor:	Hiraoka, Takuya, Tsuchida, Masaaki, Watanabe, Yotaro
Rok vydání:	2017
Předmět:	Computer Science - Artificial Intelligence Computer Science - Computation and Language
Druh dokumentu:	Working Paper
Popis:	This paper is the first attempt to learn the policy of an inquiry dialog system (IDS) by using deep reinforcement learning (DRL). Most IDS frameworks represent dialog states and dialog acts with logical formulae. In order to make learning inquiry dialog policies more effective, we introduce a logical formula embedding framework based on a recursive neural network. The results of experiments to evaluate the effect of 1) the DRL and 2) the logical formula embedding framework show that the combination of the two are as effective or even better than existing rule-based methods for inquiry dialog policies.
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1708.00667 Zobrazit plný text záznamu View this record from Arxiv