Soutenance de thèse : Alafate Abulimiti

Alafate Abulimiti (LLF)
The Role of Socio-conversational Strategies in Task-Oriented Dialogues in the Case of Peer-Tutoring Interactions: A Focus on Off-Task Talk and Hedges

This thesis explores how social language enhances learning in peer tutoring conversations between teenagers. It focuses on two key conversational phenomena - off-task talk and hedging. Off-task talk refers to casual asides about random topics during tutoring. Although temporarily distracting, this thesis shows off-task exchanges might improve student learning. Using machine learning, high-performing models were developed to automatically detect off-task talk. A computational model represents how opportunistically triggering off-task exchanges can balance educational goals and rapport. Hedging means softening speech to avoid embarrassment or discomfort. Appropriate hedging makes directive feedback gentler. This thesis exposes limitations of modern language model for nuanced social skills like hedging. Novel techniques, including re-ranking model outputs, were validated to enhance hedge generation capabilities. Feature analysis of hedge prediction models provided data-driven insights into contextual factors, like gaze patterns, influencing human hedge usage. Overall, the analytical and technical advancements expand understanding of how social language subtly shapes productive tutoring interaction.

Jury

• Prof. Justine Cassell (INRIA Paris, Carnegie Mellon University) — Directrice de thèse
• Prof. Chloé Clavel (LTCI, Institut Polytechnique de Paris, Telecom Paris) — Co-directrice de thèse
• Prof. Magalie Ochs (LIS UMR 7020 CNRS / AMU / UTLN) — Rapporteur
• Prof. Laurent Prevot (CNRS, AMU) — Rapporteur
• Prof. Gaël Guibon (Université de Lorraine, LORIA) — Examinateur
• Prof. Salvador MASCARENHAS (Ecole Normale Supérieure) — Examinateur