Proceedings

Unsupervised Modeling of Dialog Acts in Asynchronous Conversations

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Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence. Pages 1807-1812.

https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-303

Abstract

We present unsupervised approaches to the problem of modeling dialog acts in asynchronous conversations; i.e., conversations where participants collaborate with each other at different times. In particular, we investigate a graph-theoretic deterministic framework and two probabilistic conversation models (i.e., HMM and HMM+Mix) for modeling dialog acts in emails and forums. We train and test our conversation models on (a) temporal order and (b) graph-structural order of the datasets. Empirical evaluation suggests (i) the graph-theoretic framework that relies on lexical and structural similarity metrics is not the right model for this task, (ii) conversation models perform better on the graph-structural order than the temporal order of the datasets and (iii) HMM+Mix is a better conversation model than the simple HMM model.

Shafiq R. Joty, Giuseppe Carenini, and Chin-Yew Lin. 2011. Unsupervised Modeling of Dialog Acts in Asynchronous Conversations. In Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, IJCAI-11. IJCAI, 1807–1812. https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-303
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