Language models have become a key step to achieve state-of-the art results in many
different Natural Language Processing (NLP) tasks. Leveraging the huge amount of …

Contextualized word embeddings (CWE) such as provided by ELMo (Peters et al., 2018),
Flair NLP (Akbik et al., 2018), or BERT (Devlin et al., 2019) are a major recent innovation in …

In this article, we tackle the issue of the limited quantity of manually sense annotated corpora
for the task of word sense disambiguation, by exploiting the semantic relationships between …

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 …

Automatic text summarization (TS) plays a pivotal role in condensing large volumes of
information into concise, coherent summaries, facilitating efficient information retrieval and …

Contextual embeddings are proved to be overwhelmingly effective to the task of Word Sense
Disambiguation (WSD) compared with other sense representation techniques. However …

Current state-of-the-art supervised word sense disambiguation (WSD) systems (such as
GlossBERT and bi-encoder model) yield surprisingly good results by purely leveraging pre …

Distributional semantics based on neural approaches is a cornerstone of Natural Language
Processing, with surprising connections to human meaning representation as well. Recent …

In this paper, we demonstrate that by utilizing sparse word representations, it becomes
possible to surpass the results of more complex task-specific models on the task of fine …

We present WiC-TSV, a new multi-domain evaluation benchmark for Word Sense
Disambiguation. More specifically, we introduce a framework for Target Sense Verification of …