Contextual word representations derived from large-scale neural language models are
successful across a diverse set of NLP tasks, suggesting that they encode useful and …

Despite the recent success of deep neural networks in natural language processing and
other spheres of artificial intelligence, their interpretability remains a challenge. We analyze …

NLP research on logical reasoning regains momentum with the recent releases of a handful
of datasets, notably LogiQA and Reclor. Logical reasoning is exploited in many probing …

The attention mechanism is a successful technique in modern NLP, especially in tasks like
machine translation. The recently proposed network architecture of the Transformer is based …

Recent advances in NLP demonstrate the effectiveness of training large-scale language
models and transferring them to downstream tasks. Can fine-tuning these models on tasks …

Narratives are present in many forms of human expression and can be understood as a
fundamental way of communication between people. Computational understanding of the …

Neural models greatly outperform grammar-based models across many tasks in modern
computational linguistics. This raises the question of whether linguistic principles, such as …

While a lot of analysis has been carried to demonstrate linguistic knowledge captured by the
representations learned within deep NLP models, very little attention has been paid towards …

In this paper we present Uniform Meaning Representation (UMR), a meaning representation
designed to annotate the semantic content of a text. UMR is primarily based on Abstract …

A large number of studies that analyze deep neural network models and their ability to
encode various linguistic and non-linguistic concepts provide an interpretation of the inner …