@inproceedings{mathias-etal-2018-eyes,
title = "Eyes are the Windows to the Soul: Predicting the Rating of Text Quality Using Gaze Behaviour",
author = "Mathias, Sandeep and
Kanojia, Diptesh and
Patel, Kevin and
Agrawal, Samarth and
Mishra, Abhijit and
Bhattacharyya, Pushpak",
editor = "Gurevych, Iryna and
Miyao, Yusuke",
booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2018",
address = "Melbourne, Australia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P18-1219",
doi = "10.18653/v1/P18-1219",
pages = "2352--2362",
abstract = "Predicting a reader{'}s rating of text quality is a challenging task that involves estimating different subjective aspects of the text, like structure, clarity, etc. Such subjective aspects are better handled using cognitive information. One such source of cognitive information is gaze behaviour. In this paper, we show that gaze behaviour does indeed help in effectively predicting the rating of text quality. To do this, we first we model text quality as a function of three properties - organization, coherence and cohesion. Then, we demonstrate how capturing gaze behaviour helps in predicting each of these properties, and hence the overall quality, by reporting improvements obtained by adding gaze features to traditional textual features for score prediction. We also hypothesize that if a reader has fully understood the text, the corresponding gaze behaviour would give a better indication of the assigned rating, as opposed to partial understanding. Our experiments validate this hypothesis by showing greater agreement between the given rating and the predicted rating when the reader has a full understanding of the text.",
}
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<abstract>Predicting a reader’s rating of text quality is a challenging task that involves estimating different subjective aspects of the text, like structure, clarity, etc. Such subjective aspects are better handled using cognitive information. One such source of cognitive information is gaze behaviour. In this paper, we show that gaze behaviour does indeed help in effectively predicting the rating of text quality. To do this, we first we model text quality as a function of three properties - organization, coherence and cohesion. Then, we demonstrate how capturing gaze behaviour helps in predicting each of these properties, and hence the overall quality, by reporting improvements obtained by adding gaze features to traditional textual features for score prediction. We also hypothesize that if a reader has fully understood the text, the corresponding gaze behaviour would give a better indication of the assigned rating, as opposed to partial understanding. Our experiments validate this hypothesis by showing greater agreement between the given rating and the predicted rating when the reader has a full understanding of the text.</abstract>
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%0 Conference Proceedings
%T Eyes are the Windows to the Soul: Predicting the Rating of Text Quality Using Gaze Behaviour
%A Mathias, Sandeep
%A Kanojia, Diptesh
%A Patel, Kevin
%A Agrawal, Samarth
%A Mishra, Abhijit
%A Bhattacharyya, Pushpak
%Y Gurevych, Iryna
%Y Miyao, Yusuke
%S Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F mathias-etal-2018-eyes
%X Predicting a reader’s rating of text quality is a challenging task that involves estimating different subjective aspects of the text, like structure, clarity, etc. Such subjective aspects are better handled using cognitive information. One such source of cognitive information is gaze behaviour. In this paper, we show that gaze behaviour does indeed help in effectively predicting the rating of text quality. To do this, we first we model text quality as a function of three properties - organization, coherence and cohesion. Then, we demonstrate how capturing gaze behaviour helps in predicting each of these properties, and hence the overall quality, by reporting improvements obtained by adding gaze features to traditional textual features for score prediction. We also hypothesize that if a reader has fully understood the text, the corresponding gaze behaviour would give a better indication of the assigned rating, as opposed to partial understanding. Our experiments validate this hypothesis by showing greater agreement between the given rating and the predicted rating when the reader has a full understanding of the text.
%R 10.18653/v1/P18-1219
%U https://aclanthology.org/P18-1219
%U https://doi.org/10.18653/v1/P18-1219
%P 2352-2362
Markdown (Informal)
[Eyes are the Windows to the Soul: Predicting the Rating of Text Quality Using Gaze Behaviour](https://aclanthology.org/P18-1219) (Mathias et al., ACL 2018)
ACL