This repository provides materials for an ACL 2020 tutorial on Open-Domain Question Answering (QA). It targets researchers and practitioners interested in answering questions using large document collections, offering a comprehensive overview of historical context, challenges, datasets, evaluation metrics, and cutting-edge models. The benefit is a structured understanding of the ODQA landscape and key research directions.

How It Works

The tutorial covers various approaches to Open-Domain QA, including two-stage retriever-reader models, dense retrievers, end-to-end training, and retriever-free methods. It also explores hybrid approaches integrating knowledge bases with text. This structured progression from foundational concepts to advanced techniques provides a holistic view of the field.

Quick Start & Requirements

The repository contains tutorial slides and a reading list. Specific code for running models is not directly provided in this README, but links to related projects like BERTserini and Dense Passage Retrieval are implied through the reading list.

Highlighted Details

• Focuses on cutting-edge models like dense retrievers and end-to-end training.
• Discusses hybrid approaches combining text and knowledge bases.
• Provides a curated reading list of seminal and recent ODQA papers.
• Includes historical context and future research questions.

Maintenance & Community

The tutorial was held by Danqi Chen and Scott Yih. No information on ongoing maintenance or community channels is present in the README.

Licensing & Compatibility

The repository itself does not specify a license. The content is for educational purposes related to the ACL 2020 tutorial.

Limitations & Caveats

This repository serves as a collection of educational materials and a reading list from a 2020 tutorial. It does not contain runnable code or datasets, and the information may not reflect the absolute latest advancements in the rapidly evolving field of Open-Domain QA.