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 graphstructural order than the temporal order of the datasets and (iii) HMM+Mix is a better conversation model than the simple HMM model.
Unsupervised Modeling of Dialog Acts in Asynchronous Conversations
Shafiq Joty, Giuseppe Carenini, and Chin-Yew Lin. In Proceedings of the twenty second International Joint Conference on Artificial Intelligence (IJCAI'11) , pages 1807-1813, 2011.
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