Research
NLP
Answering Complex Open-domain Questions With Multi-hop Dense Retrieval
May 3, 2021
Abstract
We propose a simple and efficient multi-hop dense retrieval approach for answering complex open-domain questions, which achieves state-of-the-art performance on two multi-hop datasets, HotpotQA and multi-evidence FEVER. Contrary to previous work, our method does not require access to any corpus-specific information, such as inter-document hyperlinks or human-annotated entity markers, and can be applied to any unstructured text corpus. Our system also yields a much better efficiency-accuracy trade-off, matching the best published accuracy on HotpotQA while being 10 times faster at inference time.
AUTHORS
Written by
Wenhan Xiong
Xiang Lorraine Li
Srinivasan Iyer
Jingfei Du
Sebastian Riedel
Douwe Kiela
Barlas Oguz
Publisher
ICLR 2021
Research Topics
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