A Multi-Party Dialogue Ressource in French

TL;DR

This paper introduces DinG, a high-quality French multi-party dialogue corpus from Catan game sessions, aiming to support dialogue system research while ensuring participant privacy by focusing on game-related conversations.

Contribution

The creation of DinG, a novel French multi-party dialogue dataset with long transcriptions, and an analysis of question types to improve natural dialogue system development.

Findings

DinG provides a valuable resource for French dialogue research.

Participants focus on the game, ensuring privacy and naturalness.

Study of question types informs dialogue system design.

Abstract

We present Dialogues in Games (DinG), a corpus of manual transcriptions of real-life, oral, spontaneous multi-party dialogues between French-speaking players of the board game Catan. Our objective is to make available a quality resource for French, composed of long dialogues, to facilitate their study in the style of (Asher et al., 2016). In a general dialogue setting, participants share personal information, which makes it impossible to disseminate the resource freely and openly. In DinG, the attention of the participants is focused on the game, which prevents them from talking about themselves. In addition, we are conducting a study on the nature of the questions in dialogue, through annotation (Cruz Blandon et al., 2019), in order to develop more natural automatic dialogue systems.