Word sense disambiguation (WSD) is a process used to determine the most appropriate
meaning of a word in a given contextual framework, particularly when the word is …

Abstract Word Sense Disambiguation (WSD) is a long-standing but open problem in Natural
Language Processing (NLP). WSD corpora are typically small in size, owing to an expensive …

Transformer-based models have brought a radical change to neural machine translation. A
key feature of the Transformer architecture is the so-called multi-head attention mechanism …

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

Document-level context has received lots of attention for compensating neural machine
translation (NMT) of isolated sentences. However, recent advances in document-level NMT …

Sentiment analysis is a task that highly depends on the understanding of word senses.
Traditional neural network models are black boxes that represent word senses as vectors …

Resolving semantic ambiguity has long been recognised as a central challenge in the field
of machine translation. Recent work on benchmarking translation performance on …

This study proposes a method to improve text matching through integration of lexical
knowledge from external resources to model the senses of potentially ambiguous words …

Over the past few years, Word Sense Disambiguation (WSD) has received renewed interest:
recently proposed systems have shown the remarkable effectiveness of deep learning …

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 …