Dialogue Act Sequence Labeling using Hierarchical encoder with CRF

Understand

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.