@inproceedings{raphalen-etal-2022-might,
title = "{``}{Y}ou might think about slightly revising the title{''}: Identifying Hedges in Peer-tutoring Interactions",
author = "Raphalen, Yann and
Clavel, Chlo{\'e} and
Cassell, Justine",
editor = "Muresan, Smaranda and
Nakov, Preslav and
Villavicencio, Aline",
booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.acl-long.153",
doi = "10.18653/v1/2022.acl-long.153",
pages = "2160--2174",
abstract = "Hedges have an important role in the management of rapport. In peer-tutoring, they are notably used by tutors in dyads experiencing low rapport to tone down the impact of instructions and negative feedback. Pursuing the objective of building a tutoring agent that manages rapport with teenagers in order to improve learning, we used a multimodal peer-tutoring dataset to construct a computational framework for identifying hedges. We compared approaches relying on pre-trained resources with others that integrate insights from the social science literature. Our best performance involved a hybrid approach that outperforms the existing baseline while being easier to interpret. We employ a model explainability tool to explore the features that characterize hedges in peer-tutoring conversations, and we identify some novel features, and the benefits of a such a hybrid model approach.",
}
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<abstract>Hedges have an important role in the management of rapport. In peer-tutoring, they are notably used by tutors in dyads experiencing low rapport to tone down the impact of instructions and negative feedback. Pursuing the objective of building a tutoring agent that manages rapport with teenagers in order to improve learning, we used a multimodal peer-tutoring dataset to construct a computational framework for identifying hedges. We compared approaches relying on pre-trained resources with others that integrate insights from the social science literature. Our best performance involved a hybrid approach that outperforms the existing baseline while being easier to interpret. We employ a model explainability tool to explore the features that characterize hedges in peer-tutoring conversations, and we identify some novel features, and the benefits of a such a hybrid model approach.</abstract>
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%0 Conference Proceedings
%T “You might think about slightly revising the title”: Identifying Hedges in Peer-tutoring Interactions
%A Raphalen, Yann
%A Clavel, Chloé
%A Cassell, Justine
%Y Muresan, Smaranda
%Y Nakov, Preslav
%Y Villavicencio, Aline
%S Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F raphalen-etal-2022-might
%X Hedges have an important role in the management of rapport. In peer-tutoring, they are notably used by tutors in dyads experiencing low rapport to tone down the impact of instructions and negative feedback. Pursuing the objective of building a tutoring agent that manages rapport with teenagers in order to improve learning, we used a multimodal peer-tutoring dataset to construct a computational framework for identifying hedges. We compared approaches relying on pre-trained resources with others that integrate insights from the social science literature. Our best performance involved a hybrid approach that outperforms the existing baseline while being easier to interpret. We employ a model explainability tool to explore the features that characterize hedges in peer-tutoring conversations, and we identify some novel features, and the benefits of a such a hybrid model approach.
%R 10.18653/v1/2022.acl-long.153
%U https://aclanthology.org/2022.acl-long.153
%U https://doi.org/10.18653/v1/2022.acl-long.153
%P 2160-2174
Markdown (Informal)
[“You might think about slightly revising the title”: Identifying Hedges in Peer-tutoring Interactions](https://aclanthology.org/2022.acl-long.153) (Raphalen et al., ACL 2022)
ACL