Deep learning approaches have achieved great success in the field of Natural Language
Processing (NLP). However, directly training deep neural models often suffer from overfitting …

Neural network models have achieved state-of-the-art performances in a wide range of
natural language processing (NLP) tasks. However, a long-standing criticism against neural …

Answering questions that require multi-hop reasoning at web-scale necessitates retrieving
multiple evidence documents, one of which often has little lexical or semantic relationship to …

Question answering (QA) tasks have been posed using a variety of formats, such as
extractive span selection, multiple choice, etc. This has led to format-specialized models …

BERTs are incapable of processing long texts due to its quadratically increasing memory
and time consumption. The straightforward thoughts to address this problem, such as slicing …

In this paper, we present Hierarchical Graph Network (HGN) for multi-hop question
answering. To aggregate clues from scattered texts across multiple paragraphs, a …

Interpretable multi-hop reading comprehension (RC) over multiple documents is a
challenging problem because it demands reasoning over multiple information sources and …

Multimodal named entity recognition (MNER) is a critical step in information extraction,
which aims to detect entity spans and classify them to corresponding entity types given a …

Transformers have achieved new heights modeling natural language as a sequence of text
tokens. However, in many real world scenarios, textual data inherently exhibits structures …

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 …