Biological and artificial information processing systems form representations that they can
use to categorize, reason, plan, navigate, and make decisions. How can we measure the …

Attention mechanisms are ubiquitous components in neural architectures applied to natural
language processing. In addition to yielding gains in predictive accuracy, attention weights …

Abstract We present the Zurich Cognitive Language Processing Corpus (ZuCo), a dataset
combining electroencephalography (EEG) and eye-tracking recordings from subjects …

A lack of corpora has so far limited advances in integrating human gaze data as a
supervisory signal in neural attention mechanisms for natural language processing (NLP) …

Eye‐tracking data from reading provide a structured signal with a fine‐grained temporal
resolution which closely follows the sequential structure of the text. It is highly correlated with …

Learned self-attention functions in state-of-the-art NLP models often correlate with human
attention. We investigate whether self-attention in large-scale pre-trained language models …

While neural networks with attention mechanisms have achieved superior performance on
many natural language processing tasks, it remains unclear to which extent learned …

Nowadays, more attention has been placed on cost reductions and yield enhancement in
the semiconductor industry. During the manufacturing process, a considerable amount of …

We recorded and preprocessed ZuCo 2.0, a new dataset of simultaneous eye-tracking and
electroencephalography during natural reading and during annotation. This corpus contains …

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 …