Speaker Role Contextual Modeling for Language Understanding and Dialogue Policy Learning
Journal
8th International Joint Conference on Natural Language Processing - Proceedings of the IJCNLP 2017, System Demonstrations
Journal Volume
2
Start Page
163
End Page
168
ISBN (of the container)
978-194808702-5
Date Issued
2017
Author(s)
Abstract
Language understanding (LU) and dialogue policy learning are two essential components in conversational systems. Human-human dialogues are not well-controlled and often random and unpredictable due to their own goals and speaking habits. This paper proposes a role-based contextual model to consider different speaker roles independently based on the various speaking patterns in the multi-turn dialogues. The experiments on the benchmark dataset show that the proposed role-based model successfully learns role-specific behavioral patterns for contextual encoding and then significantly improves language understanding and dialogue policy learning tasks
Event(s)
8th International Joint Conference on Natural Language Processing, IJCNLP 2017
Publisher
Association for Computational Linguistics (ACL)
Type
conference paper