@inproceedings{wang-etal-2026-predicting,
title = "Predicting States of Understanding in Explanatory Interactions Using Cognitive Load-Related Linguistic Cues",
author = {Wang, Yu and
T{\"u}rk, Olcay and
Grimminger, Angela and
Buschmeier, Hendrik},
editor = "Piperidis, Stelios and
Bel, N{\'u}ria and
van den Heuvel, Henk and
Ide, Nancy and
Krek, Simon and
Toral, Antonio",
booktitle = "Proceedings of the Fifteenth Language Resources and Evaluation Conference",
month = may,
year = "2026",
address = "Palma de Mallorca, Spain",
publisher = "ELRA Language Resource Association",
url = "https://aclanthology.org/2026.lrec-1.889/",
doi = "10.63317/4tsmsshhd3ad",
pages = "11368--11378",
abstract = "We investigate how verbal and nonverbal linguistic features, exhibited by speakers and listeners in dialogue, can contribute to predicting the listener{'}s state of understanding in explanatory interactions on a moment-by-moment basis. Specifically, we examine three linguistic cues related to cognitive load and hypothesised to correlate with listener understanding: the information value (operationalised with surprisal) and syntactic complexity of the speaker{'}s utterances, and the variation in the listener{'}s interactive gaze behaviour. Based on statistical analyses of the MUNDEX corpus of face-to-face dialogic board game explanations, we find that individual cues vary with the listener{'}s level of understanding. Listener states ({'}Understanding', `Partial Understanding', `Non-Understanding' and `Misunderstanding') were self-annotated by the listeners using a retrospective video-recall method. The results of a subsequent classification experiment, involving two off-the-shelf classifiers and a fine-tuned German BERT-based multimodal classifier, demonstrate that prediction of these four states of understanding is generally possible and improves when the three linguistic cues are considered alongside textual features."
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<abstract>We investigate how verbal and nonverbal linguistic features, exhibited by speakers and listeners in dialogue, can contribute to predicting the listener’s state of understanding in explanatory interactions on a moment-by-moment basis. Specifically, we examine three linguistic cues related to cognitive load and hypothesised to correlate with listener understanding: the information value (operationalised with surprisal) and syntactic complexity of the speaker’s utterances, and the variation in the listener’s interactive gaze behaviour. Based on statistical analyses of the MUNDEX corpus of face-to-face dialogic board game explanations, we find that individual cues vary with the listener’s level of understanding. Listener states (’Understanding’, ‘Partial Understanding’, ‘Non-Understanding’ and ‘Misunderstanding’) were self-annotated by the listeners using a retrospective video-recall method. The results of a subsequent classification experiment, involving two off-the-shelf classifiers and a fine-tuned German BERT-based multimodal classifier, demonstrate that prediction of these four states of understanding is generally possible and improves when the three linguistic cues are considered alongside textual features.</abstract>
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%0 Conference Proceedings
%T Predicting States of Understanding in Explanatory Interactions Using Cognitive Load-Related Linguistic Cues
%A Wang, Yu
%A Türk, Olcay
%A Grimminger, Angela
%A Buschmeier, Hendrik
%Y Piperidis, Stelios
%Y Bel, Núria
%Y van den Heuvel, Henk
%Y Ide, Nancy
%Y Krek, Simon
%Y Toral, Antonio
%S Proceedings of the Fifteenth Language Resources and Evaluation Conference
%D 2026
%8 May
%I ELRA Language Resource Association
%C Palma de Mallorca, Spain
%F wang-etal-2026-predicting
%X We investigate how verbal and nonverbal linguistic features, exhibited by speakers and listeners in dialogue, can contribute to predicting the listener’s state of understanding in explanatory interactions on a moment-by-moment basis. Specifically, we examine three linguistic cues related to cognitive load and hypothesised to correlate with listener understanding: the information value (operationalised with surprisal) and syntactic complexity of the speaker’s utterances, and the variation in the listener’s interactive gaze behaviour. Based on statistical analyses of the MUNDEX corpus of face-to-face dialogic board game explanations, we find that individual cues vary with the listener’s level of understanding. Listener states (’Understanding’, ‘Partial Understanding’, ‘Non-Understanding’ and ‘Misunderstanding’) were self-annotated by the listeners using a retrospective video-recall method. The results of a subsequent classification experiment, involving two off-the-shelf classifiers and a fine-tuned German BERT-based multimodal classifier, demonstrate that prediction of these four states of understanding is generally possible and improves when the three linguistic cues are considered alongside textual features.
%R 10.63317/4tsmsshhd3ad
%U https://aclanthology.org/2026.lrec-1.889/
%U https://doi.org/10.63317/4tsmsshhd3ad
%P 11368-11378
Markdown (Informal)
[Predicting States of Understanding in Explanatory Interactions Using Cognitive Load-Related Linguistic Cues](https://aclanthology.org/2026.lrec-1.889/) (Wang et al., LREC 2026)
ACL