This paper presents the shared task on Multilingual Idiomaticity Detection and Sentence
Embedding, which consists of two subtasks:(a) a binary classification task aimed at …

Contextualised word representation models have been successfully used for capturing
different word usages and they may be an attractive alternative for representing idiomaticity …

Despite their success in a variety of NLP tasks, pre-trained language models, due to their
heavy reliance on compositionality, fail in effectively capturing the meanings of multiword …

Multiword expressions (MWEs) are composed of multiple words and exhibit variable
degrees of compositionality. As such, their meanings are notoriously difficult to model, and it …

One of the major outstanding questions in computational semantics is how humans integrate
the meaning of individual words into a sentence in a way that enables understanding of …

Accurate assessment of the ability of embedding models to capture idiomaticity may require
evaluation at token rather than type level, to account for degrees of idiomaticity and possible …

To date, transformer-based models such as BERT have been less successful in predicting
compositionality of noun compounds than static word embeddings. This is likely related to a …

Compositionality, the phenomenon where the meaning of a phrase can be derived from its
constituent parts, is a hallmark of human language. At the same time, many phrases are non …

In this paper, we apply various embedding methods on multiword expressions to study how
well they capture the nuances of non-compositional data. Our results from a pool of word …

This paper presents an exploration of different statistical association measures to
automatically identify collocations from corpora in English, Portuguese, and Spanish. To …