When to generate hedges in peer-tutoring interactions

TL;DR

This study applies machine learning to predict hedge occurrences in peer-tutoring, highlighting the importance of semantic, nonverbal, and rapport features, especially eye gaze, for accurate prediction.

Contribution

It introduces a novel approach combining semantic embeddings and nonverbal cues to predict hedging in face-to-face tutoring interactions.

Findings

Embedding layers improve model performance.

Nonverbal cues like eye gaze significantly influence hedge prediction.

Rapport and conversational features are important for accurate predictions.

Abstract

This paper explores the application of machine learning techniques to predict where hedging occurs in peer-tutoring interactions. The study uses a naturalistic face-to-face dataset annotated for natural language turns, conversational strategies, tutoring strategies, and nonverbal behaviours. These elements are processed into a vector representation of the previous turns, which serves as input to several machine learning models. Results show that embedding layers, that capture the semantic information of the previous turns, significantly improves the model's performance. Additionally, the study provides insights into the importance of various features, such as interpersonal rapport and nonverbal behaviours, in predicting hedges by using Shapley values for feature explanation. We discover that the eye gaze of both the tutor and the tutee has a significant impact on hedge prediction. We further validate this observation through a follow-up ablation study.