Paper: Mixed Membership Markov Models for Unsupervised Conversation Modeling

ACL ID D12-1009
Title Mixed Membership Markov Models for Unsupervised Conversation Modeling
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2012
Authors

Recent work has explored the use of hidden Markov models for unsupervised discourse and conversation modeling, where each seg- ment or block of text such as a message in a conversation is associated with a hidden state in a sequence. We extend this approach to al- low each block of text to be a mixture of mul- tiple classes. Under our model, the probability of a class in a text block is a log-linear func- tion of the classes in the previous block. We show that this model performs well at predic- tive tasks on two conversation data sets, im- proving thread reconstruction accuracy by up to 15 percentage points over a standard HMM. Additionally, we show quantitatively that the induced word clusters correspond to speech acts more closely than baseline models.