When to generate hedges in peer-tutoring interactions (2023)

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 behaviors. These elements are processed into a vector representation of the previous turns, which serves as input to several machine learning models, including MLP and LSTM. The results show that embedding layers, capturing 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 behaviors, 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.

## introduction effective communication involves various conversational strategies that help speakers convey their intended meaning and manage social interactions at the same time. these strategies can include the use of self-disclosure, praise, reference to shared experience, etc. @xcite . hedges are one of those strategies that is commonly used in dialogue. hedges are words or phrases that convey a degree of uncertainty or vagueness, allowing speakers to soften the impact of their statements and convey humility or modesty, or avoid face threat. although hedges can be effective in certain situations, understanding when and how to use hedges is essential and challenging. the use of hedges is especially significant in tutoring interactions where they may facilitate correcting a wrong answer without embarrassing the recipient. however, the use of hedges in this con-text is not limited to expert educators. they are also found to be abundant in peer-tutoring settings. in fact, @xcite found that confident tutors tend to use more hedges when their rapport with the tutee is low, and that this pattern leads to tutees attempting more problems and solving more problems correctly. hence, the detection and correct deployment of hedges, at the right time, is not just pleasant, but crucial for the development of effective intelligent peer tutoring systems. while the use of hedges in conversation is an important aspect of effective communication, automatically generating hedges in real-time at the right time, can be a challenging task. in recent years, there have been several studies of automatic hedge detection @xcite , particularly in the context of dialogue systems. however, despite significant advances in detection, generating hedges in a timely and appropriate manner remained unsolved. for example, the rlhf-based training method enables the development of robust language models that align with human preferences @xcite . however, this approach does not explicitly instruct large language models (e.g., chatgpt) in pragmatic and social skills, such as the appropriate use of hedges during communication. this lack of specific training can result in a gap in the model's ability to effectively integrate these conversational nuances into its responses in at the correct time. this limitation can affect the quality of communication and highlights the need for further research on effective hedge strategie generation; that is, to generate hedges at the right time. despite the widespread use of hedges in communication, there is still much to learn about their timing and the effectiveness of their use, particularly in dialogue rather than running text, and specifically in the current article, in peer-tutoring environments. to address this gap in the literature, our research focuses on two key questions: rq1: first, can we predict when hedges should be generated in peer-tutoring environments? to address this question we investigate whether it is possible to identify the points at which hedges should be introduced during a peer tutoring dialogue. ## conclusion and future work this study presents an effective approach to predict where hedges occur in peer-tutoring interactions using classic ml models. our results show the importance of considering various types of input features, such as turn embeddings, rapport, conversational strategies, tutoring strategies, nonverbal behaviors, and contextual information. moreover, shapley values applied to the predictions of the different models show, for the first time, that the gaze of both tutor and tutee may play a critical role in predicting hedges. this observation is substantiated by subsequent ablation studies, where classic classification models, like xgboost and lightgbm, experienced a significant decline in f1 score when removing nonverbal behavior features. for future work, several directions can be pursued. first, the investigation of hedge generation in the context of expert tutors could provide valuable insights into how experienced tutors use hedges differently and how these differences might affect learning outcomes. second, incorporating reinforcement learning techniques to enhance specific aspects of the interaction, such as learning performance, could improve the practical applications of our findings.