Machine Reading Comprehension (MRC) is a challenging Natural Language Processing
(NLP) research field with wide real-world applications. The great progress of this field in …

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 …

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

Aspect-based sentiment analysis (ABSA) is a fine-grained task of sentiment analysis. To
better comprehend long complicated sentences and obtain accurate aspect-specific …

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 …

Answering compositional questions that require multiple steps of reasoning against text is
challenging, especially when they involve discrete, symbolic operations. Neural module …

Though visual information has been introduced for enhancing neural machine translation
(NMT), its effectiveness strongly relies on the availability of large amounts of bilingual …

Transformers have dominated empirical machine learning models of natural language
processing. In this paper, we introduce basic concepts of Transformers and present key …

Multi-choice reading comprehension is a challenging task to select an answer from a set of
candidate options when given passage and question. Previous approaches usually only …

In the retrieval-based multi-turn dialogue modeling, it remains a challenge to select the most
appropriate response according to extracting salient features in context utterances. As a …