Open-Domain Question Answering
By Danqi Chen, Wen-tau Yih. 2020
24 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Philosophy
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Sebastian Riedel
- Patrick Lewis
- Pasquale Minervini
- Pontus Stenetorp
- Aleksandra Piktus
- Yuxiang Wu
- Hannaneh Hajishirzi
- Mohit Bansal
Citations in the corpus
- PAQ: 65 Million Probably-Asked Questions and What You Can Do With Them (2021, background): “ODQA has received much attention in both for its practical applications, and as a benchmark for how NLP models store and access knowledge (Chen and Yih, 2020; Petroni et al., 2020b).”