@inproceedings{noseworthy-etal-2017-predicting,
title = "Predicting Success in Goal-Driven Human-Human Dialogues",
author = "Noseworthy, Michael and
Cheung, Jackie Chi Kit and
Pineau, Joelle",
editor = "Jokinen, Kristiina and
Stede, Manfred and
DeVault, David and
Louis, Annie",
booktitle = "Proceedings of the 18th Annual {SIG}dial Meeting on Discourse and Dialogue",
month = aug,
year = "2017",
address = {Saarbr{\"u}cken, Germany},
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W17-5531",
doi = "10.18653/v1/W17-5531",
pages = "253--262",
abstract = "In goal-driven dialogue systems, success is often defined based on a structured definition of the goal. This requires that the dialogue system be constrained to handle a specific class of goals and that there be a mechanism to measure success with respect to that goal. However, in many human-human dialogues the diversity of goals makes it infeasible to define success in such a way. To address this scenario, we consider the task of automatically predicting success in goal-driven human-human dialogues using only the information communicated between participants in the form of text. We build a dataset from stackoverflow.com which consists of exchanges between two users in the technical domain where ground-truth success labels are available. We then propose a turn-based hierarchical neural network model that can be used to predict success without requiring a structured goal definition. We show this model outperforms rule-based heuristics and other baselines as it is able to detect patterns over the course of a dialogue and capture notions such as gratitude.",
}
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<abstract>In goal-driven dialogue systems, success is often defined based on a structured definition of the goal. This requires that the dialogue system be constrained to handle a specific class of goals and that there be a mechanism to measure success with respect to that goal. However, in many human-human dialogues the diversity of goals makes it infeasible to define success in such a way. To address this scenario, we consider the task of automatically predicting success in goal-driven human-human dialogues using only the information communicated between participants in the form of text. We build a dataset from stackoverflow.com which consists of exchanges between two users in the technical domain where ground-truth success labels are available. We then propose a turn-based hierarchical neural network model that can be used to predict success without requiring a structured goal definition. We show this model outperforms rule-based heuristics and other baselines as it is able to detect patterns over the course of a dialogue and capture notions such as gratitude.</abstract>
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%0 Conference Proceedings
%T Predicting Success in Goal-Driven Human-Human Dialogues
%A Noseworthy, Michael
%A Cheung, Jackie Chi Kit
%A Pineau, Joelle
%Y Jokinen, Kristiina
%Y Stede, Manfred
%Y DeVault, David
%Y Louis, Annie
%S Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue
%D 2017
%8 August
%I Association for Computational Linguistics
%C Saarbrücken, Germany
%F noseworthy-etal-2017-predicting
%X In goal-driven dialogue systems, success is often defined based on a structured definition of the goal. This requires that the dialogue system be constrained to handle a specific class of goals and that there be a mechanism to measure success with respect to that goal. However, in many human-human dialogues the diversity of goals makes it infeasible to define success in such a way. To address this scenario, we consider the task of automatically predicting success in goal-driven human-human dialogues using only the information communicated between participants in the form of text. We build a dataset from stackoverflow.com which consists of exchanges between two users in the technical domain where ground-truth success labels are available. We then propose a turn-based hierarchical neural network model that can be used to predict success without requiring a structured goal definition. We show this model outperforms rule-based heuristics and other baselines as it is able to detect patterns over the course of a dialogue and capture notions such as gratitude.
%R 10.18653/v1/W17-5531
%U https://aclanthology.org/W17-5531
%U https://doi.org/10.18653/v1/W17-5531
%P 253-262
Markdown (Informal)
[Predicting Success in Goal-Driven Human-Human Dialogues](https://aclanthology.org/W17-5531) (Noseworthy et al., SIGDIAL 2017)
ACL
- Michael Noseworthy, Jackie Chi Kit Cheung, and Joelle Pineau. 2017. Predicting Success in Goal-Driven Human-Human Dialogues. In Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue, pages 253–262, Saarbrücken, Germany. Association for Computational Linguistics.