Probabilistic dialogue act extraction for concept based multilingual translation systems

This paper describes a probabilistic method for dialogue act (DA) extraction for concept-based multilingual translation systems. A DA is a unit of a semantic interlingua and it consists of speaker information, speech act, concept and argument. Probabilistic models for the extraction of speech acts or concepts are trained as speech act or concept dependent word n-gram models. The proposed method is evaluated on DA-annotated English and Japanese databases. The experimental results show that the proposed method gives a better performance compared to the conventioanl grammar-based approach. In addition, the proposed method is much more robust for erroneous inputs obtained as speech recognition outputs.