Equip** machines with comprehensive knowledge of the world's entities and their
relationships has been a longstanding goal of AI. Over the last decade, large-scale …

The question answering system is frequently applied in the area of natural language
processing (NLP) because of the wide variety of applications. It consists of answering …

In this paper, we propose an end-to-end Retrieval-Augmented Visual Language Model
(REVEAL) that learns to encode world knowledge into a large-scale memory, and to retrieve …

Fact verification has attracted a lot of attention in the machine learning and natural language
processing communities, as it is one of the key methods for detecting misinformation …

Current Open-Domain Question Answering (ODQA) model paradigm often contains a
retrieving module and a reading module. Given an input question, the reading module …

Recent advances in open-domain QA have led to strong models based on dense retrieval,
but only focused on retrieving textual passages. In this work, we tackle open-domain QA …

The rise of personal assistants has made conversational question answering (ConvQA) a
very popular mechanism for user-system interaction. State-of-the-art methods for ConvQA …

Recent pretrained language models" solved" many reading comprehension benchmarks,
where questions are written with access to the evidence document. However, datasets …

Answering complex questions over knowledge bases (KB-QA) faces huge input data with
billions of facts, involving millions of entities and thousands of predicates. For efficiency, QA …