This paper presents a series of experiments in applying compositional distributional semantic models to dialogue act classifica- tion. In contrast to the widely used bag-of- words approach, we build the meaning of an utterance from its parts by composing the distributional word vectors using vec- tor addition and multiplication. We inves- tigate the contribution of word sequence, dialogue act sequence, and distributional information to the performance, and com- pare with the current state of the art ap- proaches. Our experiment suggests that that distributional information is useful for dialogue act tagging but that simple mod- els of compositionality fail to capture cru- cial information from word and utterance sequence; more advanced approaches (e.g. sequence- or grammar-driven, such as ...

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