Improving Korean Speech Acts Analysis by Using Shrinkage and Discourse Stack

A speech act is a linguistic action intended by a speaker. It is important to analyze the speech act for the dialogue understanding system because the speech act of an utterance is closely tied with the user’s intention in the utterance. This paper proposes to use a speech acts hierarchy and a discourse stack for improving the accuracy of classifiers in speech acts analysis. We first adopt a hierarchical statistical technique called shrinkage to solve the data sparseness problem. In addition, we use a discourse stack in order to easily apply discourse structure information to the speech acts analysis. From the results of experiments, we observed that the proposed model made a significant improvement for Korean speech acts analysis. Moreover, we found that it can be more useful when training data is insufficient.