Joint Turn and Dialogue level User Satisfaction Estimation on Multi-Domain Conversations
Praveen Kumar Bodigutla, Aditya Tiwari, Spyros Matsoukas, Josep Valls-Vargas, Lazaros Polymenakos
Abstract
Dialogue level quality estimation is vital for optimizing data driven dialogue management. Current automated methods to estimate turn and dialogue level user satisfaction employ hand-crafted features and rely on complex annotation schemes, which reduce the generalizability of the trained models. We propose a novel user satisfaction estimation approach which minimizes an adaptive multi-task loss function in order to jointly predict turn-level Response Quality labels provided by experts and explicit dialogue-level ratings provided by end users. The proposed BiLSTM based deep neural net model automatically weighs each turn’s contribution towards the estimated dialogue-level rating, implicitly encodes temporal dependencies, and removes the need to hand-craft features. On dialogues sampled from 28 Alexa domains, two dialogue systems and three user groups, the joint dialogue-level satisfaction estimation model achieved up to an absolute 27% (0.43 -> 0.70) and 7% (0.63 -> 0.70) improvement in linear correlation performance over baseline deep neural net and benchmark Gradient boosting regression models, respectively.- Anthology ID:
- 2020.findings-emnlp.347
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2020
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 3897–3909
- Language:
- URL:
- https://aclanthology.org/2020.findings-emnlp.347
- DOI:
- 10.18653/v1/2020.findings-emnlp.347
- Cite (ACL):
- Praveen Kumar Bodigutla, Aditya Tiwari, Spyros Matsoukas, Josep Valls-Vargas, and Lazaros Polymenakos. 2020. Joint Turn and Dialogue level User Satisfaction Estimation on Multi-Domain Conversations. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 3897–3909, Online. Association for Computational Linguistics.
- Cite (Informal):
- Joint Turn and Dialogue level User Satisfaction Estimation on Multi-Domain Conversations (Bodigutla et al., Findings 2020)
- PDF:
- https://preview.aclanthology.org/nodalida-main-page/2020.findings-emnlp.347.pdf