Open-Domain Question Answering (2020)

Abstract: This tutorial provides a comprehensive and coherent overview of cutting-edge research in open-domain question answering (QA), the task of answering questions using a large collection of documents of diversified topics. We will start by first giving a brief historical background, discussing the basic setup and core technical challenges of the research problem, and then describe modern datasets with the common evaluation metrics and benchmarks. The focus will then shift to cutting-edge models proposed for open-domain QA, including two-stage retriever-reader approaches, dense retriever and end-to-end training, and retriever-free methods. Finally, we will cover some hybrid approaches using both text and large knowledge bases and conclude the tutorial with important open questions. We hope that the tutorial will not only help the audience to acquire up-to-date knowledge but also provide new perspectives to stimulate the advances of open-domain QA research in the next phase.

## tutorial outline the intended duration of this tutorial is 3.5 hours, including a half an hour break. danqi chen danqi chen is an assistant professor of computer science at princeton university and co-directs the princeton nlp group. danqi's research interests lie within deep learning for natural language processing, with an emphasis on the intersection between text understanding and knowledge representation/reasoning and applications such as question answering and information extraction. before joining princeton university, danqi worked as a visiting scientist at facebook ai research (fair). she received her phd from stanford university (advised by christopher manning) in 2018 and b.eng from tsinghua university in 2012. website: https://www.cs.princeton.edu/ ∼danqic/. scott wen-tau yih scott wen-tau yih is a research scientist at facebook ai research (fair), and his recent research focuses on continuous representations and neural network models, with applications in knowledge base embedding, semantic parsing and question answering. yih received the best paper award from conll'11, an outstanding paper award from acl'15 and has served as an area co-chair and a program co-chair for several top conferences. he is also a co-presenter for several popular tutorials on topics including semantic role labeling, deep learning for nlp, question answering with knowledge base, web and beyond and nlp for precision medicine. website: http://scottyih.org/ .