In the NLP community, recent years have seen a surge of research activities that address
machines' ability to perform deep language understanding which goes beyond what is …

In recent years, there has been remarkable progress in leveraging Language Models (LMs),
encompassing Pre-trained Language Models (PLMs) and Large-scale Language Models …

To apply eyeshadow without a brush, should I use a cotton swab or a toothpick? Questions
requiring this kind of physical commonsense pose a challenge to today's natural language …

Given a partial description like" she opened the hood of the car," humans can reason about
the situation and anticipate what might come next (" then, she examined the engine"). In this …

The ability to understand and work with numbers (numeracy) is critical for many complex
reasoning tasks. Currently, most NLP models treat numbers in text in the same way as other …

Recent advances in commonsense reasoning depend on large-scale human-annotated
training data to achieve peak performance. However, manual curation of training examples …

We present DREAM, the first dialogue-based multiple-choice reading comprehension data
set. Collected from English as a Foreign Language examinations designed by human …

Recent success of pre-trained language models (LMs) has spurred widespread interest in
the language capabilities that they possess. However, efforts to understand whether LM …

NLP systems rarely give special consideration to numbers found in text. This starkly
contrasts with the consensus in neuroscience that, in the brain, numbers are represented …

Recent works show that pre-trained language models (PTLMs), such as BERT, possess
certain commonsense and factual knowledge. They suggest that it is promising to use …