Reading Wikipedia to Answer Open-Domain Questions

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. Our experiments on multiple existing QA
datasets indicate that (1) both modules are highly competitive with respect to
existing counterparts and (2) multitask learning using distant supervision on
their combination is an effective complete system on this challenging task.

Source: http://bppro.link/?c=QYb

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