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 …

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

We introduce a new language representation model called BERT, which stands for
Bidirectional Encoder Representations from Transformers. Unlike recent language …

Text encoding is one of the most important steps in Natural Language Processing (NLP). It
has been done well by the self-attention mechanism in the current state-of-the-art …

This study tackles generative reading comprehension (RC), which consists of answering
questions based on textual evidence and natural language generation (NLG). We propose a …

Abstract Machine reading comprehension (MRC) has drawn a lot of attention as an
approach for assessing the ability of systems to understand natural language. Usually …

Abstract Machine Reading Comprehension with unanswerable questions requires that
systems not only answer questions when possible, but also output an unanswerable …

Purpose The purpose of this paper is to describe a new approach to sentence
representation learning leading to text classification using Bidirectional Encoder …

Event argument extraction, which aims to identify arguments of specific events and label
their roles, is a challenging subtask of event extraction. Previous approaches solve this …

Adversarial training (AT) as a regularization method has proved its effectiveness in various
tasks, such as image classification and text classification. Though there are successful …