42,101 research outputs found
Teaching Machines to Read and Comprehend
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.Comment: Appears in: Advances in Neural Information Processing Systems 28
(NIPS 2015). 14 pages, 13 figure
Reading Wikipedia to Answer Open-Domain Questions
This paper proposes to tackle open- domain question answering using Wikipedia
as the unique knowledge source: the answer to any factoid question is a text
span in a Wikipedia article. This task of machine reading at scale combines the
challenges of document retrieval (finding the relevant articles) with that of
machine comprehension of text (identifying the answer spans from those
articles). Our approach combines a search component based on bigram hashing and
TF-IDF matching with a multi-layer recurrent neural network model trained to
detect answers in Wikipedia paragraphs. Our experiments on multiple existing QA
datasets indicate that (1) both modules are highly competitive with respect to
existing counterparts and (2) multitask learning using distant supervision on
their combination is an effective complete system on this challenging task.Comment: ACL2017, 10 page
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