Abstract Word Sense Disambiguation (WSD) aims at making explicit the semantics of a word
in context by identifying the most suitable meaning from a predefined sense inventory …

The intelligent manipulation of symbolic knowledge has been a long-sought goal of AI.
However, when it comes to Natural Language Processing (NLP), symbols have to be …

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 …

Pretrained Language Models (PLMs) learn rich cross-lingual knowledge and can be
finetuned to perform well on diverse tasks such as translation and multilingual word sense …

Word sense disambiguation (WSD) is the task of identifying the intended sense of a word in
context. While prior work on unsupervised WSD has leveraged lexical knowledge bases …

As a key natural language processing (NLP) task, word sense disambiguation (WSD)
evaluates how well NLP models can understand the lexical semantics of words under …

End-to-end speech translation (ST) presents notable disambiguation challenges as it
necessitates simultaneous cross-modal and cross-lingual transformations. While word …

This paper presents EvAlign, a visual analytics framework for quantitative and qualitative
evaluation of automatic translation alignment models. EvAlign offers various visualization …

Acquisition of multilingual training data continues to be a challenge in word sense
disambiguation (WSD). To address this problem, unsupervised approaches have been …

Semantic tasks are rarely formally defined, and the exact relationship between them is an
open question. We introduce a taxonomy that elucidates the connection between several …