Models that represent meaning as high-dimensional numerical vectors—such as latent
semantic analysis (LSA), hyperspace analogue to language (HAL), bound encoding of the …

As the use of deep learning techniques has grown across various fields over the past
decade, complaints about the opaqueness of the black-box models have increased …

A characteristic feature of human semantic cognition is its ability to not only store and
retrieve the properties of concepts observed through experience, but to also facilitate the …

The Empirical Methods in Natural Language Processing (EMNLP) 2018 workshop
BlackboxNLP was dedicated to resources and techniques specifically developed for …

The pervasive use of distributional semantic models or word embeddings for both cognitive
modeling and practical application is because of their remarkable ability to represent the …

There is a long record of research on equity in schools. As machine learning researchers
begin to study fairness and bias in earnest, language technologies in education have an …

Recently, the use of word embedding models (WEM) has received ample attention in the
natural language processing community. These models can capture semantic information in …

Humans carry stereotypic tacit assumptions (STAs)(Prince, 1978), or propositional beliefs
about generic concepts. Such associations are crucial for understanding natural language …

Theories of grounded cognition assume that conceptual representations are grounded in
sensorimotor experience. However, abstract concepts such as jealousy or childhood have …

Bias elimination and recent probing studies attempt to remove specific information from
embedding spaces. Here it is important to remove as much of the target information as …