English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

There is growing interest in incorporating eye-tracking data and otherimplicit measures of human language processing into natural language processing(NLP) pipelines. The data from human language processing contain unique insightinto human linguistic understanding that could be exploited by language models.However, many unanswered questions remain about the nature of this data and howit can best be utilized in downstream NLP tasks. In this paper, we presenteyeStyliency, an eye-tracking dataset for human processing of stylistic text(e.g., politeness). We develop a variety of methods to derive style saliencyscores over text using the collected eye dataset. We further investigate howthis saliency data compares to both human annotation methods and model-basedinterpretability metrics. We find that while eye-tracking data is unique, italso intersects with both human annotations and model-based importance scores,providing a possible bridge between human- and machine-based perspectives. Wepropose utilizing this type of data to evaluate the cognitive plausibility ofmodels that interpret style. Our eye-tracking data and processing code arepublicly available.

A Comparative Study on Textual Saliency of Styles from Eye Tracking, Annotations, and Language Models