Improved multi-kernel SVM for multi-modal and imbalanced dialogue act classification

Dialogue act recognition is recognized as an important step for computers to understand human dialogues as it is closely related to the human intention. There are two main challenges in dialogue act recognition. Firstly, multimodal features should be taken into consideration, which include lexical, syntactic, prosodic cues, even facial appearance and gesture. Secondly, samples distribution in the dialogue act corpus is highly imbalanced. Thus traditional classification algorithms produce poor performance when they are applied on these imbalanced multi-modal tasks. In this paper, the multi-kernel SVM model is investigated to deal with these problems. Multi-kernel SVM is an effective technique for leaning from multi-modal data, but it is sensitive to imbalance. So an improved multi-kernel SVM model is proposed. To show the effectiveness of the proposed model, we test it on some open classification tasks and a Chinese dialogue act recognition task. Significant improvements are observed from the experimental results.