AI is widely thought to be poised to transform business, yet current perceptions of the scope
of this transformation may be myopic. Recent progress in natural language processing …

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 …

Bidirectional Encoder Representations from Transformers (BERT) has shown marvelous
improvements across various NLP tasks, and consecutive variants have been proposed to …

Increasing model size when pretraining natural language representations often results in
improved performance on downstream tasks. However, at some point further model …

With the capability of modeling bidirectional contexts, denoising autoencoding based
pretraining like BERT achieves better performance than pretraining approaches based on …

Bidirectional Encoder Representations from Transformers (BERT) has shown marvelous
improvements across various NLP tasks, and its consecutive variants have been proposed …

The latest work on language representations carefully integrates contextualized features into
language model training, which enables a series of success especially in various machine …

Understanding narratives requires reading between the lines, which in turn, requires
interpreting the likely causes and effects of events, even when they are not mentioned …

Abstract Machine reading comprehension (MRC) is an AI challenge that requires machines
to determine the correct answers to questions based on a given passage. MRC systems …

Compared to traditional machine learning, deep learning can learn deeper abstract data
representation and understand scattered data properties. It has gained considerable …