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        <title>A Comparative Study on Textual Saliency of Styles from Eye Tracking, Annotations, and Language Models</title>
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    <abstract>There is growing interest in incorporating eye-tracking data and other implicit measures of human language processing into natural language processing (NLP) pipelines. The data from human language processing contain unique insight into human linguistic understanding that could be exploited by language models. However, many unanswered questions remain about the nature of this data and how it can best be utilized in downstream NLP tasks. In this paper, we present EyeStyliency, an eye-tracking dataset for human processing of stylistic text (e.g., politeness). We develop an experimental protocol to collect these style-specific eye movements. We further investigate how this saliency data compares to both human annotation methods and model-based interpretability metrics. We find that while eye-tracking data is unique, it also intersects with both human annotations and model-based importance scores, providing a possible bridge between human- and machine-based perspectives. We propose utilizing this type of data to evaluate the cognitive plausibility of models that interpret style. Our eye-tracking data and processing code are publicly available.</abstract>
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%0 Conference Proceedings
%T A Comparative Study on Textual Saliency of Styles from Eye Tracking, Annotations, and Language Models
%A de Langis, Karin
%A Kang, Dongyeop
%Y Jiang, Jing
%Y Reitter, David
%Y Deng, Shumin
%S Proceedings of the 27th Conference on Computational Natural Language Learning (CoNLL)
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F de-langis-kang-2023-comparative
%X There is growing interest in incorporating eye-tracking data and other implicit measures of human language processing into natural language processing (NLP) pipelines. The data from human language processing contain unique insight into human linguistic understanding that could be exploited by language models. However, many unanswered questions remain about the nature of this data and how it can best be utilized in downstream NLP tasks. In this paper, we present EyeStyliency, an eye-tracking dataset for human processing of stylistic text (e.g., politeness). We develop an experimental protocol to collect these style-specific eye movements. We further investigate how this saliency data compares to both human annotation methods and model-based interpretability metrics. We find that while eye-tracking data is unique, it also intersects with both human annotations and model-based importance scores, providing a possible bridge between human- and machine-based perspectives. We propose utilizing this type of data to evaluate the cognitive plausibility of models that interpret style. Our eye-tracking data and processing code are publicly available.
%R 10.18653/v1/2023.conll-1.8
%U https://aclanthology.org/2023.conll-1.8
%U https://doi.org/10.18653/v1/2023.conll-1.8
%P 108-121

Markdown (Informal)

[A Comparative Study on Textual Saliency of Styles from Eye Tracking, Annotations, and Language Models](https://aclanthology.org/2023.conll-1.8) (de Langis & Kang, CoNLL 2023)

• A Comparative Study on Textual Saliency of Styles from Eye Tracking, Annotations, and Language Models (de Langis & Kang, CoNLL 2023)

ACL

• Karin de Langis and Dongyeop Kang. 2023. A Comparative Study on Textual Saliency of Styles from Eye Tracking, Annotations, and Language Models. In Proceedings of the 27th Conference on Computational Natural Language Learning (CoNLL), pages 108–121, Singapore. Association for Computational Linguistics.