@inproceedings{finch-etal-2021-went,
title = "What Went Wrong? Explaining Overall Dialogue Quality through Utterance-Level Impacts",
author = "Finch, James D. and
Finch, Sarah E. and
Choi, Jinho D.",
booktitle = "Proceedings of the 3rd Workshop on Natural Language Processing for Conversational AI",
month = nov,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.nlp4convai-1.9",
doi = "10.18653/v1/2021.nlp4convai-1.9",
pages = "93--101",
abstract = "Improving user experience of a dialogue system often requires intensive developer effort to read conversation logs, run statistical analyses, and intuit the relative importance of system shortcomings. This paper presents a novel approach to automated analysis of conversation logs that learns the relationship between user-system interactions and overall dialogue quality. Unlike prior work on utterance-level quality prediction, our approach learns the impact of each interaction from the overall user rating without utterance-level annotation, allowing resultant model conclusions to be derived on the basis of empirical evidence and at low cost. Our model identifies interactions that have a strong correlation with the overall dialogue quality in a chatbot setting. Experiments show that the automated analysis from our model agrees with expert judgments, making this work the first to show that such weakly-supervised learning of utterance-level quality prediction is highly achievable.",
}
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%0 Conference Proceedings
%T What Went Wrong? Explaining Overall Dialogue Quality through Utterance-Level Impacts
%A Finch, James D.
%A Finch, Sarah E.
%A Choi, Jinho D.
%S Proceedings of the 3rd Workshop on Natural Language Processing for Conversational AI
%D 2021
%8 nov
%I Association for Computational Linguistics
%C Online
%F finch-etal-2021-went
%X Improving user experience of a dialogue system often requires intensive developer effort to read conversation logs, run statistical analyses, and intuit the relative importance of system shortcomings. This paper presents a novel approach to automated analysis of conversation logs that learns the relationship between user-system interactions and overall dialogue quality. Unlike prior work on utterance-level quality prediction, our approach learns the impact of each interaction from the overall user rating without utterance-level annotation, allowing resultant model conclusions to be derived on the basis of empirical evidence and at low cost. Our model identifies interactions that have a strong correlation with the overall dialogue quality in a chatbot setting. Experiments show that the automated analysis from our model agrees with expert judgments, making this work the first to show that such weakly-supervised learning of utterance-level quality prediction is highly achievable.
%R 10.18653/v1/2021.nlp4convai-1.9
%U https://aclanthology.org/2021.nlp4convai-1.9
%U https://doi.org/10.18653/v1/2021.nlp4convai-1.9
%P 93-101
Markdown (Informal)
[What Went Wrong? Explaining Overall Dialogue Quality through Utterance-Level Impacts](https://aclanthology.org/2021.nlp4convai-1.9) (Finch et al., NLP4ConvAI 2021)
ACL