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

We present the Eyetracked Multi-Modal Translation (EMMT) corpus, a datasetcontaining monocular eye movement recordings, audio and 4-electrodeelectroencephalogram (EEG) data of 43 participants. The objective was tocollect cognitive signals as responses of participants engaged in a number oflanguage intensive tasks involving different text-image stimuli settings whentranslating from English to Czech.  Each participant was exposed to 32 text-image stimuli pairs and asked to (1)read the English sentence, (2) translate it into Czech, (3) consult the image,(4) translate again, either updating or repeating the previous translation. Thetext stimuli consisted of 200 unique sentences with 616 unique words coupledwith 200 unique images as the visual stimuli.  The recordings were collected over a two week period and all the participantsincluded in the study were Czech natives with strong English skills. Due to thenature of the tasks involved in the study and the relatively large number ofparticipants involved, the corpus is well suited for research in TranslationProcess Studies, Cognitive Sciences among other disciplines.

EMMT: A simultaneous eye-tracking, 4-electrode EEG and audio corpus for multi-modal reading and translation scenarios