What Speakers really Mean when they Ask Questions: Classification of Intentions with a Supervised Approach

This paper focuses on the automatic detection of hidden intentions of speakers in questions asked during meals. Our corpus is composed of a set of transcripts of spontaneous oral conversations from ESLO{'}s corpora. We suggest a typology of these intentions based on our research work and the exploration and annotation of the corpus, in which we define two {``}explicit{''} categories (request for agreement and request for information) and three {``}implicit{''} categories (opinion, will and doubt). We implement a supervised automatic classification model based on annotated data and selected linguistic features and we evaluate its results and performances. We finally try to interpret these results by looking more deeply and specifically into the predictions of the algorithm and the features it used. There are many motivations for this work which are part of ongoing challenges such as opinion analysis, irony detection or the development of conversational agents.