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

arXiv: https://arxiv.org/abs/1806.11420 · HTML: https://mlsanity.com/paper/1806.11420/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jun 2018.

## Built on by (most cited first, 2 of 2)

- [Conversational Analysis of Daily Dialog Data using Polite Emotional Dialogue Acts](https://mlsanity.com/paper/2205.02921/) (2022)
- [Polite Emotional Dialogue Acts for Conversational Analysis in Daily Dialog Data](https://mlsanity.com/paper/2112.13572/) (2021)

## Abstract (arXiv)

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.