  Dialogue Act (DA) classification is the task of classifying utterances with
respect to the function they serve in a dialogue. Existing approaches to DA
classification model utterances without incorporating the turn changes among
speakers throughout the dialogue, therefore treating it no different than
non-interactive written text. In this paper, we propose to integrate the turn
changes in conversations among speakers when modeling DAs. Specifically, we
learn conversation-invariant speaker turn embeddings to represent the speaker
turns in a conversation; the learned speaker turn embeddings are then merged
with the utterance embeddings for the downstream task of DA classification.
With this simple yet effective mechanism, our model is able to capture the
semantics from the dialogue content while accounting for different speaker
turns in a conversation. Validation on three benchmark public datasets
demonstrates superior performance of our model.
