Abstract
We investigate the potential of adversarial evaluation methods for open-domain dialogue generation systems, comparing the performance of a discriminative agent to that of humans on the same task. Our results show that the task is hard, both for automated models and humans, but that a discriminative agent can learn patterns that lead to above-chance performance.- Anthology ID:
- W17-5534
- Volume:
- Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue
- Month:
- August
- Year:
- 2017
- Address:
- Saarbrücken, Germany
- Editors:
- Kristiina Jokinen, Manfred Stede, David DeVault, Annie Louis
- Venue:
- SIGDIAL
- SIG:
- SIGDIAL
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 284–288
- Language:
- URL:
- https://aclanthology.org/W17-5534
- DOI:
- 10.18653/v1/W17-5534
- Cite (ACL):
- Elia Bruni and Raquel Fernández. 2017. Adversarial evaluation for open-domain dialogue generation. In Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue, pages 284–288, Saarbrücken, Germany. Association for Computational Linguistics.
- Cite (Informal):
- Adversarial evaluation for open-domain dialogue generation (Bruni & Fernández, SIGDIAL 2017)
- PDF:
- https://preview.aclanthology.org/bionlp-24-ingestion/W17-5534.pdf