Teaching Machines to Read and Comprehend

@inproceedings{Hermann2015TeachingMT,
  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},
  booktitle={NIPS},
  year={2015}
}
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 a number of papers. REVIEW HIGHLY INFLUENTIAL CITATIONS
Highly Cited
This paper has 815 citations. REVIEW CITATIONS

Extracted Numerical Results

  • The chart shows the precision for each decile in document lengths across the corpus as well as the precision for the 5% longest articles.

Topics

Statistics

01002003002015201620172018
Citations per Year

815 Citations

Semantic Scholar estimates that this publication has 815 citations based on the available data.

See our FAQ for additional information.