Unsupervised Modeling of Dialog Acts in Asynchronous Conversations
By Shafiq Joty, UBC CS
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. 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).