Dialog Act Tagging Using Graphical Models

Abstract

Detecting discourse patterns, such as dialog acts (DAs), is an important factor for processing spoken conversations and meetings. Different techniques, such as hidden Markov models and neural networks, have been used to tag dialog acts in the past. A full analysis of dialog act tagging using different generative and conditional dynamic Bayesian networks (DBNs) is performed, where both conventional switching n-grams and factored language models (FLMs) are used as DBN edge implementations. Our tests on the ICSI meeting recorder dialog act (MRDA) corpus show that the factored language model implementations are better than the switching n-gram approach. Our results also show that by using virtual evidence, the label bias problem in conditional models can be avoided. Also, we find that, on a corpus such as MRDA, using the dialog acts of previous sentences to help predict current words does not improve our conditional model.