Departing from traditional linguistic models, advances in deep learning have resulted in a
new type of predictive (autoregressive) deep language models (DLMs). Using a self …

Scientific studies of language behavior need to grapple with a large diversity of languages in
the world and, for reading, a further variability in writing systems. Yet, the ability to form …

An interesting method of evaluating word representations is by how much they reflect the
semantic representations in the human brain. However, most, if not all, previous works only …

We analyze if large language models are able to predict patterns of human reading
behavior. We compare the performance of language-specific and multilingual pretrained …

This report introduces the Beijing Sentence Corpus (BSC). This is a Chinese sentence
corpus of eye-tracking data with relatively clear word boundaries. In addition, we report …

Departing from classical rule-based linguistic models, advances in deep learning have led
to the development of a new family of self-supervised deep language models (DLMs). These …

Eye-tracking data from reading represent an important resource for both linguistics and
natural language processing. The ability to accurately model gaze features is crucial to …

The current work presents the very first eye-tracking corpus of natural reading by Chinese-
English bilinguals, whose two languages entail different writing systems and orthographies …

In this study, we present TURead, an eye movement dataset of silent and oral sentence
reading in Turkish, an agglutinative language with a shallow orthography understudied in …

Determining the relative importance of the elements in a sentence is a key factor for
effortless natural language understanding. For human language processing, we can …