  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 on large-scale unstructured documents. Recently, there
has been a surge in the amount of research literature on OpenQA, particularly
on techniques that integrate with neural Machine Reading Comprehension (MRC).
While these research works have advanced performance to new heights on
benchmark datasets, they have been rarely covered in existing surveys on QA
systems. In this work, we review the latest research trends in OpenQA, with
particular attention to systems that incorporate neural MRC techniques.
Specifically, we begin with revisiting the origin and development of OpenQA
systems. We then introduce modern OpenQA architecture named "Retriever-Reader"
and analyze the various systems that follow this architecture as well as the
specific techniques adopted in each of the components. We then discuss key
challenges to developing OpenQA systems and offer an analysis of benchmarks
that are commonly used. We hope our work would enable researchers to be
informed of the recent advancement and also the open challenges in OpenQA
research, so as to stimulate further progress in this field.
