2 citations · 4 across the 4 of their papers we have counts for
4 papers
How to Dissect a Muppet: The Structure of Transformer Embedding Spaces
Timothee Mickus, Denis Paperno, Mathieu Constant
Pretrained embeddings based on the Transformer architecture have taken the NLP community by storm. We show that they can mathematically be reframed as a sum of vector factors and s…
Semeval-2022 Task 1: CODWOE -- Comparing Dictionaries and Word Embeddings
Timothee Mickus, Kees van Deemter, Mathieu Constant +1
Word embeddings have advanced the state of the art in NLP across numerous tasks. Understanding the contents of dense neural representations is of utmost interest to the computation…
A Game Interface to Study Semantic Grounding in Text-Based Models
Timothee Mickus, Mathieu Constant, Denis Paperno
Can language models learn grounded representations from text distribution alone? This question is both central and recurrent in natural language processing; authors generally agree…
What do you mean, BERT? Assessing BERT as a Distributional Semantics Model
Timothee Mickus, Denis Paperno, Mathieu Constant +1
Contextualized word embeddings, i.e. vector representations for words in context, are naturally seen as an extension of previous noncontextual distributional semantic models. In th…