Neural Response Generation for Customer Service based on Personality Traits

Jonathan Herzig, Michal Shmueli-Scheuer, Tommy Sandbank, David Konopnicki


Abstract
We present a neural response generation model that generates responses conditioned on a target personality. The model learns high level features based on the target personality, and uses them to update its hidden state. Our model achieves performance improvements in both perplexity and BLEU scores over a baseline sequence-to-sequence model, and is validated by human judges.
Anthology ID:
W17-3541
Volume:
Proceedings of the 10th International Conference on Natural Language Generation
Month:
September
Year:
2017
Address:
Santiago de Compostela, Spain
Venues:
INLG | WS
SIG:
SIGGEN
Publisher:
Association for Computational Linguistics
Note:
Pages:
252–256
Language:
URL:
https://www.aclweb.org/anthology/W17-3541
DOI:
10.18653/v1/W17-3541
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PDF:
http://aclanthology.lst.uni-saarland.de/W17-3541.pdf