Building and Exploiting a Corpus of Dialog Interactions between French Speaking Virtual and Human Agents

Abstract

We describe the acquisition of a dialog corpus for French based on multi-task human-machine interactions in a serious game setting. We present a tool for data collection that is configurable for multiple games; describe the data collected using this tool and the annotation schema used to annotate it; and report on the results obtained when training a classifier on the annotated data to associate each player turn with a dialog move usable by a rule based dialog manager. The collected data consists of approximately 1250 dialogs, 10454 utterances and 168509 words and will be made freely available to academic and nonprofit research.

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