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Teaching Machines to Read and Comprehend

Karl Moritz Hermann
Tomas Kocisky
Edward Grefenstette
Will Kay
Mustafa Suleyman
Phil Blunsom
NIPS (2015) (to appear)
Google Scholar


Teaching machines to read natural language documents remains an elusive chal- lenge. Such models can be tested on their ability to answer questions posed on the contents of the documents that they have seen, but until now large scale su- pervised training and test datasets have been missing for such tasks. In this work we introduce a new machine reading paradigm based on large scale supervised training datasets extracted from readily available online sources. We define mod- els for this task based on both a traditional natural language processing pipeline, and on attention based recurrent neural networks. Our results demonstrate that neural network models are able to learn to read documents and answer complex questions with minimal prior knowledge of language structure.

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