Abstract
Although deep learning models have brought tremendous advancements to the field of open-domain dialogue response generation, recent research results have revealed that the trained models have undesirable generation behaviors, such as malicious responses and generic (boring) responses. In this work, we propose a framework named “Negative Training” to minimize such behaviors. Given a trained model, the framework will first find generated samples that exhibit the undesirable behavior, and then use them to feed negative training signals for fine-tuning the model. Our experiments show that negative training can significantly reduce the hit rate of malicious responses, or discourage frequent responses and improve response diversity.- Anthology ID:
- 2020.acl-main.185
- Volume:
- Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2020
- Address:
- Online
- Editors:
- Dan Jurafsky, Joyce Chai, Natalie Schluter, Joel Tetreault
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2044–2058
- Language:
- URL:
- https://aclanthology.org/2020.acl-main.185
- DOI:
- 10.18653/v1/2020.acl-main.185
- Cite (ACL):
- Tianxing He and James Glass. 2020. Negative Training for Neural Dialogue Response Generation. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 2044–2058, Online. Association for Computational Linguistics.
- Cite (Informal):
- Negative Training for Neural Dialogue Response Generation (He & Glass, ACL 2020)
- PDF:
- https://preview.aclanthology.org/ingest-2024-clasp/2020.acl-main.185.pdf
- Code
- cloudygoose/negativetraining_acl2020