Open-domain Question Answering (OpenQA) is an important task in Natural Language
Processing (NLP), which aims to answer a question in the form of natural language based …

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 …

Generative models for open domain question answering have proven to be competitive,
without resorting to external knowledge. While promising, this approach requires to use …

Large pre-trained language models have been shown to store factual knowledge in their
parameters, and achieve state-of-the-art results when fine-tuned on downstream NLP tasks …

We study the problem of injecting knowledge into large pre-trained models like BERT and
RoBERTa. Existing methods typically update the original parameters of pre-trained models …

Open Domain Question Answering (QA) is evolving from complex pipelined systems to end-
to-end deep neural networks. Specialized neural models have been developed for …

In this paper, we propose a new paradigm for the task of entity-relation extraction. We cast
the task as a multi-turn question answering problem, ie, the extraction of entities and …

We consider the problem of adapting neural paragraph-level question answering models to
the case where entire documents are given as input. Our proposed solution trains models to …

We consider open-domain queston answering (QA) where answers are drawn from either a
corpus, a knowledge base (KB), or a combination of both of these. We focus on a setting in …

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 …