Unsupervised Modeling of Dialog Acts in Asynchronous Conversations
By Shafiq Joty, UBC CS
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. This is a joint work with Dr. Giuseppe Carenini (UBC) and Dr. Chin-Yew Lin (MSRA), and has been accepted in IJCAI 2011 (talk and poster).

Visit the LCI Forum page