@inproceedings{bodigutla-etal-2020-joint,
title = "Joint Turn and Dialogue level User Satisfaction Estimation on Multi-Domain Conversations",
author = "Bodigutla, Praveen Kumar and
Tiwari, Aditya and
Matsoukas, Spyros and
Valls-Vargas, Josep and
Polymenakos, Lazaros",
editor = "Cohn, Trevor and
He, Yulan and
Liu, Yang",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.findings-emnlp.347",
doi = "10.18653/v1/2020.findings-emnlp.347",
pages = "3897--3909",
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 -{\textgreater} 0.70) and 7{\%} (0.63 -{\textgreater} 0.70) improvement in linear correlation performance over baseline deep neural net and benchmark Gradient boosting regression models, respectively.",
}
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<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 -\textgreater 0.70) and 7% (0.63 -\textgreater 0.70) improvement in linear correlation performance over baseline deep neural net and benchmark Gradient boosting regression models, respectively.</abstract>
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%0 Conference Proceedings
%T Joint Turn and Dialogue level User Satisfaction Estimation on Multi-Domain Conversations
%A Bodigutla, Praveen Kumar
%A Tiwari, Aditya
%A Matsoukas, Spyros
%A Valls-Vargas, Josep
%A Polymenakos, Lazaros
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Findings of the Association for Computational Linguistics: EMNLP 2020
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F bodigutla-etal-2020-joint
%X 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 -\textgreater 0.70) and 7% (0.63 -\textgreater 0.70) improvement in linear correlation performance over baseline deep neural net and benchmark Gradient boosting regression models, respectively.
%R 10.18653/v1/2020.findings-emnlp.347
%U https://aclanthology.org/2020.findings-emnlp.347
%U https://doi.org/10.18653/v1/2020.findings-emnlp.347
%P 3897-3909
Markdown (Informal)
[Joint Turn and Dialogue level User Satisfaction Estimation on Multi-Domain Conversations](https://aclanthology.org/2020.findings-emnlp.347) (Bodigutla et al., Findings 2020)
ACL