Teaching Machines to Read and Comprehend
Abstract
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.
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.
Research Areas
