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 …

Machine reading comprehension (MRC), which requires a machine to answer questions
based on a given context, has attracted increasing attention with the incorporation of various …

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

We introduce SummScreen, a summarization dataset comprised of pairs of TV series
transcripts and human written recaps. The dataset provides a challenging testbed for …

We present the first human-annotated dialogue-based relation extraction (RE) dataset
DialogRE, aiming to support the prediction of relation (s) between two arguments that …

Reading strategies have been shown to improve comprehension levels, especially for
readers lacking adequate prior knowledge. Just as the process of knowledge accumulation …

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 …

Research into the area of multiparty dialog has grown considerably over recent years. We
present the Molweni dataset, a machine reading comprehension (MRC) dataset with …

Machine reading comprehension (MRC) aims to teach machines to read and comprehend
human languages, which is a long-standing goal of natural language processing (NLP) …

We propose a new task for assessing machines' skills of understanding fictional characters
in narrative stories. The task, TVShowGuess, builds on the scripts of TV series and takes the …