Question answering

Question Answering (QA) requires a machine to answer the given question by reading documents, graphs or web pages. It is not only useful for applications such as search engines and chat-bots, but also a meaningful test bed for evaluating machines' ability to understand and reason over natural language text. Our group leads the state-of-the-art techniques for various question answering tasks, such as a reading comprehension model with bi-directional attention flow, a real-time open-domain QA model, a sequence generation model for math equations, and a question decomposition model for multi-hop QA. We have also developed useful resources such as real-time demos of reading comprehension, open-domain QA & multi-hop QA, and datasets for GRE math problems with finegrained supervision, so please give it a try!

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