Over the past years, distributed semantic representations have proved to be effective and
flexible keepers of prior knowledge to be integrated into downstream applications. This …

Pre-trained language models (LMs) encode rich information about linguistic structure but
their knowledge about lexical polysemy remains unclear. We propose a novel experimental …

Transformer-based architectures brought a breeze of change to Word Sense
Disambiguation (WSD), improving models' performances by a large margin. The fast …

Transformer-based language models have taken many fields in NLP by storm. BERT and its
derivatives dominate most of the existing evaluation benchmarks, including those for Word …

The knowledge acquisition bottleneck strongly affects the creation of multilingual sense-
annotated data, hence limiting the power of supervised systems when applied to multilingual …

Abstract Word Sense Disambiguation (WSD) is the task of identifying the meaning of a word
in a given context. It lies at the base of Natural Language Processing as it provides semantic …

The well-known problem of knowledge acquisition is one of the biggest issues in Word
Sense Disambiguation (WSD), where annotated data are still scarce in English and almost …

Abstract Word Sense Disambiguation (WSD), ie, the task of assigning senses to words in
context, has seen a surge of interest with the advent of neural models and a considerable …

Recent studies have shed some light on a common pitfall of Neural Machine Translation
(NMT) models, stemming from their struggle to disambiguate polysemous words without …

Abstract Word Sense Disambiguation (WSD) is the task of associating the correct meaning
with a word in a given context. WSD provides explicit semantic information that is beneficial …