Unsupervised Counselor Dialogue Clustering for Positive Emotion Elicitation in Neural Dialogue System

Nurul Lubis, Sakriani Sakti, Koichiro Yoshino, Satoshi Nakamura


Abstract
Positive emotion elicitation seeks to improve user’s emotional state through dialogue system interaction, where a chat-based scenario is layered with an implicit goal to address user’s emotional needs. Standard neural dialogue system approaches still fall short in this situation as they tend to generate only short, generic responses. Learning from expert actions is critical, as these potentially differ from standard dialogue acts. In this paper, we propose using a hierarchical neural network for response generation that is conditioned on 1) expert’s action, 2) dialogue context, and 3) user emotion, encoded from user input. We construct a corpus of interactions between a counselor and 30 participants following a negative emotional exposure to learn expert actions and responses in a positive emotion elicitation scenario. Instead of relying on the expensive, labor intensive, and often ambiguous human annotations, we unsupervisedly cluster the expert’s responses and use the resulting labels to train the network. Our experiments and evaluation show that the proposed approach yields lower perplexity and generates a larger variety of responses.
Anthology ID:
W18-5017
Volume:
Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue
Month:
July
Year:
2018
Address:
Melbourne, Australia
Venues:
SIGDIAL | WS
SIG:
SIGDIAL
Publisher:
Association for Computational Linguistics
Note:
Pages:
161–170
Language:
URL:
https://www.aclweb.org/anthology/W18-5017
DOI:
10.18653/v1/W18-5017
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PDF:
http://aclanthology.lst.uni-saarland.de/W18-5017.pdf