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
- Venue:
- INLG
- SIG:
- SIGGEN
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 252–256
- Language:
- URL:
- https://aclanthology.org/W17-3541
- DOI:
- 10.18653/v1/W17-3541
- Cite (ACL):
- Jonathan Herzig, Michal Shmueli-Scheuer, Tommy Sandbank, and David Konopnicki. 2017. Neural Response Generation for Customer Service based on Personality Traits. In Proceedings of the 10th International Conference on Natural Language Generation, pages 252–256, Santiago de Compostela, Spain. Association for Computational Linguistics.
- Cite (Informal):
- Neural Response Generation for Customer Service based on Personality Traits (Herzig et al., INLG 2017)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/W17-3541.pdf