Title: Dialogue Act Sequence Labeling using Hierarchical encoder with CRF,
Abstract: Dialogue Act recognition associate dialogue acts (i.e., semantic labels) to
utterances in a conversation. The problem of associating semantic labels to
utterances can be treated as a sequence labeling problem. In this work, we
build a hierarchical recurrent neural network using bidirectional LSTM as a
base unit and the conditional random field (CRF) as the top layer to classify
each utterance into its corresponding dialogue act. The hierarchical network
learns representations at multiple levels, i.e., word level, utterance level,
and conversation level. The conversation level representations are input to the
CRF layer, which takes into account not only all previous utterances but also
their dialogue acts, thus modeling the dependency among both, labels and
utterances, an important consideration of natural dialogue. We validate our
approach on two different benchmark data sets, Switchboard and Meeting Recorder
Dialogue Act, and show performance improvement over the state-of-the-art
methods by $2.2\%$ and $4.1\%$ absolute points, respectively. It is worth
noting that the inter-annotator agreement on Switchboard data set is $84\%$,
and our method is able to achieve the accuracy of about $79\%$ despite being
trained on the noisy data.