Improving social relationships in face-to-face human-agent interactions: when the agent wants to know user's likes and dislikes

Caroline Langlet and ChloƩ Clavel


Abstract

This paper tackles the issue of the detection of user's likes and dislikes in a human-agent interaction. We present a system handling the interaction issue by jointly processing agent's and user's utterances. It is designed as a rule-based and bottom-up process based on a symbolic representation of the structure of the sentence. This article also describes the annotation campaign

-- carried out through Amazon Mechanical Turk -- for the creation of the evaluation data-set. Finally, we present all measures for rating agreement between our system and the human reference and obtain agreement scores that correspond at least to substantial agreements.