Open-domain question answering is the task of question answering on open-domain datasets such as Wikipedia.
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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.
Machine comprehension (MC), answering a query about a given context paragraph, requires modeling complex interactions between the context and the query.
#4 best model for Question Answering on MS MARCO
We publicly release a new large-scale dataset, called SearchQA, for machine comprehension, or question-answering.
We present SpanBERT, a pre-training method that is designed to better represent and predict spans of text.
SOTA for Question Answering on NewsQA
Distantly supervised open-domain question answering (DS-QA) aims to find answers in collections of unlabeled text.
#2 best model for Open-Domain Question Answering on Quasar
In this paper we look at a more practical setting, namely QA over the combination of a KB and entity-linked text, which is appropriate when an incomplete KB is available with a large text corpus.
Existing open-domain question answering (QA) models are not suitable for real-time usage because they need to process several long documents on-demand for every input query.