Joint Turn and Dialogue level User Satisfaction Estimation on Multi-Domain Conversations

• URL: http://arxiv.org/abs/2010.02495v2
• Date: Thu, 8 Oct 2020 21:10:47 GMT
• Title: Joint Turn and Dialogue level User Satisfaction Estimation on Multi-Domain Conversations
• Authors: Praveen Kumar Bodigutla, Aditya Tiwari, Josep Valls Vargas, Lazaros Polymenakos, Spyros Matsoukas
• Abstract summary: Current automated methods to estimate turn and dialogue level user satisfaction employ hand-crafted features. We propose a novel user satisfaction estimation approach which minimizes an adaptive multi-task loss function. The BiLSTM based deep neural net model automatically weighs each turn's contribution towards the estimated dialogue-level rating.
• Score: 6.129731338249762
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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