Reading Wikipedia to Answer Open-Domain Questions

ACL 2017 Danqi Chen • Adam Fisch • Jason Weston • Antoine Bordes

This paper proposes to tackle open- domain question answering using Wikipedia as the unique knowledge source: the answer to any factoid question is a text span in a Wikipedia article. This task of machine reading at scale combines the challenges of document retrieval (finding the relevant articles) with that of machine comprehension of text (identifying the answer spans from those articles). Our approach combines a search component based on bigram hashing and TF-IDF matching with a multi-layer recurrent neural network model trained to detect answers in Wikipedia paragraphs.

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Task Dataset Model Metric name Metric value Global rank Compare
Question Answering SQuAD1.1 Document Reader (single model) EM 70.733 # 120
Question Answering SQuAD1.1 Document Reader (single model) F1 79.353 # 120