Discourse-Wizard: Discovering Deep Discourse Structure in your Conversation with RNNs

TL;DR

Discourse-Wizard introduces neural models that analyze dialogue structure by considering context, providing a live demo for understanding deep discourse patterns in conversations.

Contribution

This work presents the development of neural models that incorporate context for dialogue act classification and offers a live demo tool for discourse analysis.

Findings

Context improves dialogue understanding in neural models.

Live demo facilitates exploration of discourse structures.

Models distinguish between utterance-level and context-aware analysis.

Abstract

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.