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.
|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|