Human similarity and relatedness judgements between concepts underlie most of cognitive
capabilities, such as categorisation, memory, decision-making and reasoning. For this …

The ability to control for the kinds of information encoded in neural representation has a
variety of use cases, especially in light of the challenge of interpreting these models. We …

Abstract Machine learning about language can be improved by supplying it with specific
knowledge and sources of external information. We present here a new version of the linked …

Extensive evaluation on a large number of word embedding models for language
processing applications is conducted in this work. First, we introduce popular word …

We study the topmost weight matrix of neural network language models. We show that this
matrix constitutes a valid word embedding. When training language models, we recommend …

Word associations have been used widely in psychology, but the validity of their application
strongly depends on the number of cues included in the study and the extent to which they …

Word embedding models have become a fundamental component in a wide range of
Natural Language Processing (NLP) applications. However, embeddings trained on human …

Context-predicting models (more commonly known as embeddings or neural language
models) are the new kids on the distributional semantics block. Despite the buzz …

In this paper, we provide a theoretical understanding of word embedding and its
dimensionality. Motivated by the unitary-invariance of word embedding, we propose the …

We re-evaluate the standard practice of sharing weights between input and output
embeddings in state-of-the-art pre-trained language models. We show that decoupled …