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Dialogue Act recognition associate dialogue acts (i.e., semantic labels) toutterances in a conversation. The problem of associating semantic labels toutterances can be treated as a sequence labeling problem. In this work, webuild a hierarchical recurrent neural network using bidirectional LSTM as abase unit and the conditional random field (CRF) as the top layer to classifyeach utterance into its corresponding dialogue act. The hierarchical networklearns representations at multiple levels, i.e., word level, utterance level,and conversation level. The conversation level representations are input to theCRF layer, which takes into account not only all previous utterances but alsotheir dialogue acts, thus modeling the dependency among both, labels andutterances, an important consideration of natural dialogue. We validate ourapproach on two different benchmark data sets, Switchboard and Meeting RecorderDialogue Act, and show performance improvement over the state-of-the-artmethods by $2.2\%$ and $4.1\%$ absolute points, respectively. It is worthnoting that the inter-annotator agreement on Switchboard data set is $84\%$,and our method is able to achieve the accuracy of about $79\%$ despite beingtrained on the noisy data.

Dialogue Act Sequence Labeling using Hierarchical encoder with CRF