Unsupervised Approach for Dialogue Act Classification

This paper presents an unsupervised approach for dialogue act (DA) classification. We used a latent variable model to compress the dimensions of the feature vector. We introduced a paraphraser to reduce the variety of expressions and to solve the pragmatic problem for DA classification. The paraphraser seemed to work well on some DA classifications in the unsupervised approach. The results obtained by the unsupervised approach were compared with the manually annotated labels. A preliminary experiment for semi-supervised tagging was also carried out, and we discuss these results.