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3 months ago

Answering Complex Open-Domain Questions with Multi-Hop Dense Retrieval

Answering Complex Open-Domain Questions with Multi-Hop Dense Retrieval

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.

Code Repositories

facebookresearch/multihop_dense_retrieval
Official
pytorch
Mentioned in GitHub

Benchmarks

BenchmarkMethodologyMetrics
question-answering-on-hotpotqaRecursive Dense Retriever
ANS-EM: 0.623
ANS-F1: 0.753
JOINT-EM: 0.418
JOINT-F1: 0.666
SUP-EM: 0.575
SUP-F1: 0.809

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