Sentiment analysis is an increasingly evolving field of research in computer science. With
the considerable number of studies on innovative sentiment analysis available, it is worth …

Large Language Models (LLMs) have seen significant use in domains such as natural
language processing and computer vision. Going beyond text, image and graphics, LLMs …

The translation of brain dynamics into natural language is pivotal for brain-computer
interfaces (BCIs), a field that has seen substantial growth in recent years. With the swift …

State-of-the-art brain-to-text systems have achieved great success in decoding language
directly from brain signals using neural networks. However, current approaches are limited …

Learning attention functions requires large volumes of data, but many NLP tasks simulate
human behavior, and in this paper, we show that human attention really does provide a …

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 …

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 …

Decoding text stimuli from cognitive signals (eg fMRI) enhances our understanding of the
human language system, paving the way for building versatile Brain-Computer Interface …

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 …

The translation of brain dynamics into natural language is pivotal for brain-computer
interfaces (BCIs), a field that has seen substantial growth in recent years. With the swift …