Teaching Machines to Read and Comprehend

  title={Teaching Machines to Read and Comprehend},
  author={Karl Moritz Hermann and Tom{\'a}s Kocisk{\'y} and Edward Grefenstette and Lasse Espeholt and Will Kay and Mustafa Suleyman and Phil Blunsom},
Teaching machines to read natural language documents remains an elusive challenge. Machine reading systems can be tested on their ability to answer questions posed on the contents of documents that they have seen, but until now large scale training and test datasets have been missing for this type of evaluation. In this work we define a new methodology that resolves this bottleneck and provides large scale supervised reading comprehension data. This allows us to develop a class of attention… CONTINUE READING
Highly Influential
This paper has highly influenced 126 other papers. REVIEW HIGHLY INFLUENTIAL CITATIONS
Highly Cited
This paper has 1,095 citations. REVIEW CITATIONS


Publications citing this paper.
Showing 1-10 of 703 citations

1,095 Citations

Citations per Year
Semantic Scholar estimates that this publication has 1,095 citations based on the available data.

See our FAQ for additional information.