Towards Coherent and Engaging Spoken Dialog Response Generation Using Automatic Conversation Evaluators
Sanghyun Yi, Rahul Goel, Chandra Khatri, Alessandra Cervone, Tagyoung Chung, Behnam Hedayatnia, Anu Venkatesh, Raefer Gabriel, Dilek Hakkani-Tur
Abstract
Encoder-decoder based neural architectures serve as the basis of state-of-the-art approaches in end-to-end open domain dialog systems. Since most of such systems are trained with a maximum likelihood (MLE) objective they suffer from issues such as lack of generalizability and the generic response problem, i.e., a system response that can be an answer to a large number of user utterances, e.g., “Maybe, I don’t know.” Having explicit feedback on the relevance and interestingness of a system response at each turn can be a useful signal for mitigating such issues and improving system quality by selecting responses from different approaches. Towards this goal, we present a system that evaluates chatbot responses at each dialog turn for coherence and engagement. Our system provides explicit turn-level dialog quality feedback, which we show to be highly correlated with human evaluation. To show that incorporating this feedback in the neural response generation models improves dialog quality, we present two different and complementary mechanisms to incorporate explicit feedback into a neural response generation model: reranking and direct modification of the loss function during training. Our studies show that a response generation model that incorporates these combined feedback mechanisms produce more engaging and coherent responses in an open-domain spoken dialog setting, significantly improving the response quality using both automatic and human evaluation.- Anthology ID:
- W19-8608
- Volume:
- Proceedings of the 12th International Conference on Natural Language Generation
- Month:
- October–November
- Year:
- 2019
- Address:
- Tokyo, Japan
- Editors:
- Kees van Deemter, Chenghua Lin, Hiroya Takamura
- Venue:
- INLG
- SIG:
- SIGGEN
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 65–75
- Language:
- URL:
- https://aclanthology.org/W19-8608
- DOI:
- 10.18653/v1/W19-8608
- Cite (ACL):
- Sanghyun Yi, Rahul Goel, Chandra Khatri, Alessandra Cervone, Tagyoung Chung, Behnam Hedayatnia, Anu Venkatesh, Raefer Gabriel, and Dilek Hakkani-Tur. 2019. Towards Coherent and Engaging Spoken Dialog Response Generation Using Automatic Conversation Evaluators. In Proceedings of the 12th International Conference on Natural Language Generation, pages 65–75, Tokyo, Japan. Association for Computational Linguistics.
- Cite (Informal):
- Towards Coherent and Engaging Spoken Dialog Response Generation Using Automatic Conversation Evaluators (Yi et al., INLG 2019)
- PDF:
- https://preview.aclanthology.org/naacl-24-ws-corrections/W19-8608.pdf