A Context-based Approach for Dialogue Act Recognition using Simple Recurrent Neural Networks
This work addresses the problem of context-sensitivity in dialogue act recognition for natural language understanding, but it is incremental as it builds on existing methods by adding context.
The authors tackled dialogue act recognition by proposing a context-based learning method, which improved detection on the Switchboard corpus by considering preceding utterances as context.
Dialogue act recognition is an important part of natural language understanding. We investigate the way dialogue act corpora are annotated and the learning approaches used so far. We find that the dialogue act is context-sensitive within the conversation for most of the classes. Nevertheless, previous models of dialogue act classification work on the utterance-level and only very few consider context. We propose a novel context-based learning method to classify dialogue acts using a character-level language model utterance representation, and we notice significant improvement. We evaluate this method on the Switchboard Dialogue Act corpus, and our results show that the consideration of the preceding utterances as a context of the current utterance improves dialogue act detection.