Where have we been, and where are we going? It is easier to talk about the past than the
future. These days, benchmarks evolve more bottom up (such as papers with code). There …

Data augmentation is an important component in the robustness evaluation of models in
natural language processing (NLP) and in enhancing the diversity of the data they are …

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 …

Building meaningful phrase representations is challenging because phrase meanings are
not simply the sum of their constituent meanings. Lexical composition can shift the meanings …

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 …

We discuss the problem of interpreting noun compounds such as colon cancer tumor
suppressor protein, which pose major challenges for the automatic interpretation of English …

Nominal compounds such as red wine and nut case display a continuum of compositionality,
with varying contributions from the components of the compound to its semantics. This article …

A human-inspired, linguistically sophisticated model of language understanding for
intelligent agent systems. One of the original goals of artificial intelligence research was to …

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 …

Distributional semantic models (DSMs) are often evaluated on artificial similarity datasets
containing single words or fully compositional phrases. We present a large-scale …