论文信息 - Chat-Like Conversational System Based on Selection of Reply Generating Module with Reinforcement Learning

Chat-Like Conversational System Based on Selection of Reply Generating Module with Reinforcement Learning

This paper presents a chat-like conversational system, and that generates a reply by selecting an appropriate reply generating module. Such modules consist in selecting a sentence from an article of Web news, retrieving a definition sentence in Wikipedia, question-answering, and so on. A dialogue strategy corresponds to which reply generating module should be chosen according to a user input and the dialogue history, and is learned in the MDP framework. User evaluations showed that our system could learn an appropriate dialogue strategy, and perform natural dialogues.

[1] Milica Gasic,et al. The Hidden Information State model: A practical framework for POMDP-based spoken dialogue management , 2010, Comput. Speech Lang..

[2] Roberto Pieraccini,et al. Using Markov decision process for learning dialogue strategies , 1998, Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181).

[3] Leslie Pack Kaelbling,et al. Planning and Acting in Partially Observable Stochastic Domains , 1998, Artif. Intell..

[4] R. Bellman. A Markovian Decision Process , 1957 .

[5] Ryuichiro Higashinaka,et al. Controlling Listening-oriented Dialogue using Partially Observable Markov Decision Processes , 2010, COLING.

[6] Tatsuya Kawahara,et al. Spoken Dialogue System based on Information Extraction using Similarity of Predicate Argument Structures , 2011, SIGDIAL Conference.

[7] Mikio Nakano,et al. A Framework for Building Conversational Agents Based on a Multi-Expert Model , 2008, SIGDIAL Workshop.

[8] Joseph Weizenbaum,et al. and Machine , 1977 .