# When to generate hedges in peer-tutoring interactions

arXiv: https://arxiv.org/abs/2307.15582 · HTML: https://mlsanity.com/paper/2307.15582/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 44 references that are in the map.
- First posted on arXiv 28 Jul 2023.

## Builds on (most cited first, 14 of 14)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [The many Shapley values for model explanation](https://mlsanity.com/paper/1908.08474/) (2019)
- [MIME: MIMicking Emotions for Empathetic Response Generation](https://mlsanity.com/paper/2010.01454/) (2020)
- [Estimation-Action-Reflection: Towards Deep Interaction Between Conversational and Recommender Systems](https://mlsanity.com/paper/2002.09102/) (2020)
- [MoEL: Mixture of Empathetic Listeners](https://mlsanity.com/paper/1908.07687/) (2019)
- [Generating Empathetic Responses by Looking Ahead the User's Sentiment](https://mlsanity.com/paper/1906.08487/) (2019)
- [Dialogue Act Classification with Context-Aware Self-Attention](https://mlsanity.com/paper/1904.02594/) (2019)
- [Studying Alignment in a Collaborative Learning Activity via Automatic Methods: The Link Between What We Say and Do](https://mlsanity.com/paper/2104.04429/) (2021)
- ["You might think about slightly revising the title": identifying hedges in peer-tutoring interactions](https://mlsanity.com/paper/2306.14911/) (2023)
- [How About Kind of Generating Hedges using End-to-End Neural Models?](https://mlsanity.com/paper/2306.14696/) (2023)

## Built on by (most cited first, 1 of 1)

- [Training LLMs to Recognize Hedges in Spontaneous Narratives](https://mlsanity.com/paper/2408.03319/) (2024)

## Abstract (arXiv)

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.