Discourse-Wizard is to demonstrate the context in spoken language which comes from their sequential patterns. For example, a sentence is formed by a sequence of words, a conversation is formed by a sequence of utterances, and so on. Dialogue act represents a performative action of an utterance. There have been many approaches to model such a deep contextual-concept to analyse conversations. The neural approaches have been deployed and achieved state-of-the-art results. In this demonstration recurrent neural networks are used to model the context-based learning of dialogue acts.
As you can see in the small piece of conversation example, Utt2 and Utt4 are very same (in this case they are same “Yeah”), however, the dialogue acts are very different. As they come from their context utterances Utt1 and Utt3 respectively. If it appears after a ‘Yes-No Question’ dialogue act it is more likely to have ‘Yes-Answer’ rather than ‘Backchannel’ or any other dialogue act. The presented example is from the Switchboard Dialogue Act (SwDA) Corpus, which is annotated with 42 such dialogue acts.
The context-based approach, which can take the preceding utterance (or utterances) into account, is crucial for language understanding modules in any dialogue engines. We use the hierarchical recurrent neural networks (RNN) to model such a context for conversational analysis as shown in the followind slides.
Bothe, C., Weber, C., Magg, S., and Wermter, S. (2020).
"EDA: Enriching Emotional Dialogue Acts using an Ensemble of Neural Annotators".
Proceedings of the Language Resources and Evaluation Conference (LREC-2020).
Bothe, C., Weber, C., Magg, S., and Wermter, S. (2019).
"Enriching Existing Conversational Emotion Datasets with Dialogue Acts using Neural Annotators".
Final version above in the Proceedings of the Language Resources and Evaluation Conference (LREC-2020).
Bothe, C., Magg, S., Weber, C., and Wermter, S. (2018).
Discourse-Wizard: Discovering Deep Discourse Structure in your Conversation with RNNs.
arXiv:1806.11420 [cs.CL]
Bothe, C., Magg, S., Weber, C., and Wermter, S. (2018).
Conversational Analysis using Utterance-level Attention-based Bidirectional Recurrent Neural Networks.
Proceedings of INTERSPEECH 2018.
Bothe, C., Weber, C., Magg, S., and Wermter, S. (2018).
A Context-based Approach for Dialogue Act Recognition using Simple Recurrent Neural Networks.
Proceedings of the Language Resources and Evaluation Conference (LREC-2018).