AAAI Publications, Twenty-Second International Joint Conference on Artificial Intelligence

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Unsupervised Modeling of Dialog Acts in Asynchronous Conversations
Shafiq Rayhan Joty, Giuseppe Carenini, Chin-Yew Lin

Last modified: 2011-06-28


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.

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