Alongside huge volumes of research on deep learning models in NLP in the recent years,
there has been much work on benchmark datasets needed to track modeling progress …

In the last few years, a large number of automatic evaluation metrics have been proposed for
evaluating Natural Language Generation (NLG) systems. The rapid development and …

Humans gather information through conversations involving a series of interconnected
questions and answers. For machines to assist in information gathering, it is therefore …

Machine reading is a fundamental task for testing the capability of natural language
understanding, which is closely related to human cognition in many aspects. With the rising …

Natural language understanding involves reading between the lines with implicit
background knowledge. Current systems either rely on pre-trained language models as the …

Dialogue systems have achieved growing success in many areas thanks to the rapid
advances of machine learning techniques. In the quest for generating more human-like …

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

Understanding time is crucial for understanding events expressed in natural language.
Because people rarely say the obvious, it is often necessary to have commonsense …

Abstract Machine reading comprehension (MRC) is a crucial and challenging task in NLP.
Recently, pre-trained language models (LMs), especially BERT, have achieved remarkable …

Automatic KB completion for commonsense knowledge graphs (eg, ATOMIC and
ConceptNet) poses unique challenges compared to the much studied conventional …