# Teaching Machines to Read and Comprehend

arXiv: https://arxiv.org/abs/1506.03340 · HTML: https://mlsanity.com/paper/1506.03340/

## Key facts

- Cited by 1,049 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jun 2015.

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## Abstract (arXiv)

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 based deep neural networks that learn to read real documents and answer complex questions with minimal prior knowledge of language structure.