6 citations · 12 across the 5 of their papers we have counts for
8 papers
Generating image captions with external encyclopedic knowledge
Sofia Nikiforova, Tejaswini Deoskar, Denis Paperno +1
Accurately reporting what objects are depicted in an image is largely a solved problem in automatic caption generation. The next big challenge on the way to truly humanlike caption…
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 Meaning-Form Correlation Has to Compose With
Timothee Mickus, Timothée Bernard, Denis Paperno
Compositionality is a widely discussed property of natural languages, although its exact definition has been elusive. We focus on the proposal that compositionality can be assessed…
Mark my Word: A Sequence-to-Sequence Approach to Definition Modeling
Timothee Mickus, Denis Paperno, Mathieu Constant
Defining words in a textual context is a useful task both for practical purposes and for gaining insight into distributed word representations. Building on the distributional hypot…
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…