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

摘要

This paper tackles the issue of the detection of user's verbal expressions of likes and dislikes in a human-agent interaction. We present a system grounded on the theoretical framework provided by (Martin and White, 2005) that integrates the interaction context 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 are equal or higher than substantial agreements.