Machine Reading Comprehension (MRC) is a challenging task and hot topic in Natural
Language Processing. The goal of this field is to develop systems for answering the …

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 …

Pretrained multilingual models are able to perform cross-lingual transfer in a zero-shot
setting, even for languages unseen during pretraining. However, prior work evaluating …

Abstract Natural Language Inference (NLI) and Semantic Textual Similarity (STS) are widely
used benchmark tasks for compositional evaluation of pre-trained language models. Despite …

Datasets are foundational to many breakthroughs in modern artificial intelligence. Many
recent achievements in the space of natural language processing (NLP) can be attributed to …

Fake news detection is a challenging problem in online social media, with considerable
social and political impacts. Several methods have already been proposed for the automatic …

Despite the progress made in recent years in addressing natural language understanding
(NLU) challenges, the majority of this progress remains to be concentrated on resource-rich …

Over a decade, the research field of computational linguistics has witnessed the growth of
corpora and models for natural language inference (NLI) for rich-resource languages such …

Little attention has been paid to the development of human language technology for truly
low-resource languages—ie, languages with limited amounts of digitally available text data …

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 …