Discourse-Wizard: Discovering Deep Discourse Structure in your Conversation with RNNs
This work addresses the need for better context-aware models in dialogue systems, but it appears incremental as it builds on existing neural network approaches without introducing major innovations.
The authors tackled the problem of spoken language understanding in dialogue systems by demonstrating the importance of context, developing two neural models (one without context and one with context) and making them available through a live demo called Discourse-Wizard.
Spoken language understanding is one of the key factors in a dialogue system, and a context in a conversation plays an important role to understand the current utterance. In this work, we demonstrate the importance of context within the dialogue for neural network models through an online web interface live demo. We developed two different neural models: a model that does not use context and a context-based model. The no-context model classifies dialogue acts at an utterance-level whereas the context-based model takes some preceding utterances into account. We make these trained neural models available as a live demo called Discourse-Wizard using a modular server architecture. The live demo provides an easy to use interface for conversational analysis and for discovering deep discourse structures in a conversation.