Recent advances in language representation using neural networks have made it viable to
transfer the learned internal states of large pretrained language models (LMs) to …

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 …

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 …

As the capabilities of language models continue to advance, it is conceivable that “one-size-
fits-all” model will remain as the main paradigm. For instance, given the vast number of …

In natural language processing (NLP), there is a need for more resources in Portuguese,
since much of the data used in the state-of-the-art research is in other languages. In this …

To advance the neural encoding of Portuguese (PT), and a fortiori the technological
preparation of this language for the digital age, we developed a Transformer-based …

Traditional text classification approaches often require a good amount of labeled data, which
is difficult to obtain, especially in restricted domains or less widespread languages. This lack …

The strategy of training the model from scratch in a specific language or domain serves two
essential purposes: i) enhancing performance in the particular linguistic or domain context …

Natural language inference (NLI), also known as textual entailment recognition (TER), is a
crucial task in natural language processing that combines many fundamental aspects of …

Recent advances in language representation using neural networks have made it viable to
transfer the learned internal states of large pretrained language models (LMs) to …