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

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 dataset. 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.

• Type: Journal
• Year: 2015
• Where: ACL
• Authors: Caroline Langlet, Chloé Clavel
• Keywords: ACL 2015, Computational Linguistics