Identifying Collaborative Conversations using Latent Discourse Behaviors

Ayush Jain, Maria Leonor Pacheco, Steven Lancette, Mahak Goindani, Dan Goldwasser


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.
Anthology ID:
2020.sigdial-1.10
Volume:
Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue
Month:
July
Year:
2020
Address:
1st virtual meeting
Venue:
SIGDIAL
SIG:
SIGDIAL
Publisher:
Association for Computational Linguistics
Note:
Pages:
74–78
Language:
URL:
https://aclanthology.org/2020.sigdial-1.10
DOI:
Bibkey:
Cite (ACL):
Ayush Jain, Maria Leonor Pacheco, Steven Lancette, Mahak Goindani, and Dan Goldwasser. 2020. Identifying Collaborative Conversations using Latent Discourse Behaviors. In Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 74–78, 1st virtual meeting. Association for Computational Linguistics.
Cite (Informal):
Identifying Collaborative Conversations using Latent Discourse Behaviors (Jain et al., SIGDIAL 2020)
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PDF:
https://aclanthology.org/2020.sigdial-1.10.pdf