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
- Editors:
- Olivier Pietquin, Smaranda Muresan, Vivian Chen, Casey Kennington, David Vandyke, Nina Dethlefs, Koji Inoue, Erik Ekstedt, Stefan Ultes
- Venue:
- SIGDIAL
- SIG:
- SIGDIAL
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 74–78
- Language:
- URL:
- https://aclanthology.org/2020.sigdial-1.10
- DOI:
- 10.18653/v1/2020.sigdial-1.10
- 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)
- PDF:
- https://preview.aclanthology.org/bionlp-24-ingestion/2020.sigdial-1.10.pdf