Despite the tremendous recent progress on natural language inference (NLI), driven largely
by large-scale investment in new datasets (eg, SNLI, MNLI) and advances in modeling, most …

Natural language inference (NLI) and semantic textual similarity (STS) are key tasks in
natural language understanding (NLU). Although several benchmark datasets for those …

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 …

Current research in natural language processing is highly dependent on carefully produced
corpora. Most existing resources focus on English; some resources focus on languages …

One common trend in recent studies of language models (LMs) is the use of standardized
tests for evaluation. However, despite being the fifth most spoken language worldwide, few …

Textual Entailment Recognition (TER), also known as natural language inference, is a
crucial task in natural language processing that combines many fundamental aspects of …

Recognizing textual entailment comprises the task of determining semantic entailment
relations between text fragments. A text fragment entails another text fragment if, from the …

Leveraging research on the neural modelling of Portuguese, we contribute a collection of
datasets for an array of language processing tasks and a corresponding collection of fine …

Distinct pre-trained embedding models perform differently in sentence similarity recognition
tasks. The current assumption is that they encode different features due to differences in …

Paraphrasing is a fundamental technique for many text applications. Typically, this task is
performed through models that perform lexical and translation operations, which tend to …