Recent approaches for dialogue act recognition have shown that context from
preceding utterances is important to classify the subsequent one. It was shown
that the performance improves rapidly when the context is taken into account.
We propose an utterance-level attention-based bidirectional recurrent neural
network (Utt-Att-BiRNN) model to analyze the importance of preceding utterances
to classify the current one. In our setup, the BiRNN is given the input set of
current and preceding utterances. Our model outperforms previous models that
use only preceding utterances as context on the used corpus. Another
contribution of the article is to discover the amount of information in each
utterance to classify the subsequent one and to show that context-based
learning not only improves the performance but also achieves higher confidence
in the classification. We use character- and word-level features to represent
the utterances. The results are presented for character and word feature
representations and as an ensemble model of both representations. We found that
when classifying short utterances, the closest preceding utterances contributes
to a higher degree.