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.

翻译：本文探讨了运用机器学习技术预测同伴辅导互动中模糊限制语出现位置的问题。研究采用自然情境下的面对面互动数据集，该数据包含自然语言话轮、会话策略、辅导策略及非语言行为的标注。这些要素被处理成前序话轮的向量表征，作为多个机器学习模型的输入。结果表明，能够捕捉前序话轮语义信息的嵌入层显著提升了模型性能。此外，研究通过夏普利值进行特征解释，揭示了人际融洽度与非语言行为等特征在模糊限制语预测中的重要性。我们发现辅导者与被辅导者的目光注视对模糊限制语预测具有显著影响，并通过后续的消融研究进一步验证了这一观察结果。