Distributional semantics has deeply changed in the last decades. First, predict models stole
the thunder from traditional count ones, and more recently both of them were replaced in …

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 …

Background Semantic textual similarity (STS) is a natural language processing (NLP) task
that involves assigning a similarity score to 2 snippets of text based on their meaning. This …

Large-scale pretrained language models are the major driving force behind recent
improvements in performance on the Winograd Schema Challenge, a widely employed test …

This book presents a taxonomy framework and survey of methods relevant to explaining the
decisions and analyzing the inner workings of Natural Language Processing (NLP) models …

Eye movement recordings from reading are one of the richest signals of human language
processing. Corpora of eye movements during reading of contextualized running text is a …

Character-level information is included in many NLP models, but evaluating the information
encoded in character representations is an open issue. We leverage perceptual …

Given the fast development of analysis techniques for NLP and speech processing systems,
few systematic studies have been conducted to compare the strengths and weaknesses of …

How do neural networks" perceive" speech sounds from unknown languages? Does the
typological similarity between the model's training language (L1) and an unknown language …

Large language models (LLMs) exhibit remarkable performance improvement through in-
context learning (ICL) by leveraging task-specific examples in the input. However, the …