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
Venues:
EMNLP | Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3897–3909
Language:
URL:
https://www.aclweb.org/anthology/2020.findings-emnlp.347
DOI:
10.18653/v1/2020.findings-emnlp.347
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.findings-emnlp.347.pdf