@inproceedings{jain-etal-2020-identifying,
title = "Identifying Collaborative Conversations using Latent Discourse Behaviors",
author = "Jain, Ayush and
Pacheco, Maria Leonor and
Lancette, Steven and
Goindani, Mahak and
Goldwasser, Dan",
editor = "Pietquin, Olivier and
Muresan, Smaranda and
Chen, Vivian and
Kennington, Casey and
Vandyke, David and
Dethlefs, Nina and
Inoue, Koji and
Ekstedt, Erik and
Ultes, Stefan",
booktitle = "Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
month = jul,
year = "2020",
address = "1st virtual meeting",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.sigdial-1.10",
doi = "10.18653/v1/2020.sigdial-1.10",
pages = "74--78",
abstract = "In this work, we study collaborative online conversations. Such conversations are rich in content, constructive and motivated by a shared goal. Automatically identifying such conversations requires modeling complex discourse behaviors, which characterize the flow of information, sentiment and community structure within discussions. To help capture these behaviors, we define a hybrid relational model in which relevant discourse behaviors are formulated as discrete latent variables and scored using neural networks. These variables provide the information needed for predicting the overall collaborative characterization of the entire conversational thread. We show that adding inductive bias in the form of latent variables results in performance improvement, while providing a natural way to explain the decision.",
}
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%0 Conference Proceedings
%T Identifying Collaborative Conversations using Latent Discourse Behaviors
%A Jain, Ayush
%A Pacheco, Maria Leonor
%A Lancette, Steven
%A Goindani, Mahak
%A Goldwasser, Dan
%Y Pietquin, Olivier
%Y Muresan, Smaranda
%Y Chen, Vivian
%Y Kennington, Casey
%Y Vandyke, David
%Y Dethlefs, Nina
%Y Inoue, Koji
%Y Ekstedt, Erik
%Y Ultes, Stefan
%S Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue
%D 2020
%8 July
%I Association for Computational Linguistics
%C 1st virtual meeting
%F jain-etal-2020-identifying
%X In this work, we study collaborative online conversations. Such conversations are rich in content, constructive and motivated by a shared goal. Automatically identifying such conversations requires modeling complex discourse behaviors, which characterize the flow of information, sentiment and community structure within discussions. To help capture these behaviors, we define a hybrid relational model in which relevant discourse behaviors are formulated as discrete latent variables and scored using neural networks. These variables provide the information needed for predicting the overall collaborative characterization of the entire conversational thread. We show that adding inductive bias in the form of latent variables results in performance improvement, while providing a natural way to explain the decision.
%R 10.18653/v1/2020.sigdial-1.10
%U https://aclanthology.org/2020.sigdial-1.10
%U https://doi.org/10.18653/v1/2020.sigdial-1.10
%P 74-78
Markdown (Informal)
[Identifying Collaborative Conversations using Latent Discourse Behaviors](https://aclanthology.org/2020.sigdial-1.10) (Jain et al., SIGDIAL 2020)
ACL