Difference between revisions of "2015 TeachingMachinestoReadandCompre"

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* 1. Ellen Riloff, Michael Thelen, A Rule-based Question Answering System for Reading Comprehension Tests, Proceedings of the 2000 ANLP/NAACL Workshop on Reading Comprehension Tests As Evaluation for Computer-based Language Understanding Sytems, p.13-19, May 04-04, 2000, Seattle, Washington
 
* 1. Ellen Riloff, Michael Thelen, A Rule-based Question Answering System for Reading Comprehension Tests, Proceedings of the 2000 ANLP/NAACL Workshop on Reading Comprehension Tests As Evaluation for Computer-based Language Understanding Sytems, p.13-19, May 04-04, 2000, Seattle, Washington
* 2. Hoifung Poon, Janara Christensen, Pedro Domingos, [[Oren Etzioni]], Raphael Hoffmann, Chloe Kiddon, Thomas Lin, Xiao Ling, Mausam, Alan Ritter, Stefan Schoenmackers, Stephen Soderland, Dan Weld, Fei Wu, Congle Zhang, Machine Reading at the University of Washington, Proceedings of the NAACL HLT 2010 First International Workshop on Formalisms and Methodology for Learning by Reading, p.87-95, June 06-06, 2010, Los Angeles, California
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* 2. Hoifung Poon, Janara Christensen, [[Pedro Domingos]], [[Oren Etzioni]], Raphael Hoffmann, Chloe Kiddon, Thomas Lin, Xiao Ling, Mausam, Alan Ritter, Stefan Schoenmackers, Stephen Soderland, Dan Weld, Fei Wu, Congle Zhang, Machine Reading at the University of Washington, Proceedings of the NAACL HLT 2010 First International Workshop on Formalisms and Methodology for Learning by Reading, p.87-95, June 06-06, 2010, Los Angeles, California
 
* 3. Jason Weston, Sumit Chopra, and Antoine Bordes. Memory Networks. <i>CoRR</i>, Abs/1410.3916, 2014.
 
* 3. Jason Weston, Sumit Chopra, and Antoine Bordes. Memory Networks. <i>CoRR</i>, Abs/1410.3916, 2014.
 
* 4. Sainbayar Sukhbaatar, Arthur Szlam, Jason Weston, and Rob Fergus. End-to-end Memory Networks. <i>CoRR</i>, Abs/1503.08895, 2015.
 
* 4. Sainbayar Sukhbaatar, Arthur Szlam, Jason Weston, and Rob Fergus. End-to-end Memory Networks. <i>CoRR</i>, Abs/1503.08895, 2015.

Latest revision as of 05:39, 16 August 2019

Subject Headings: Attention-Based QA-LSTM; Attention-Based QA-LSTM-CNN; Attention Mechanism; Deep LSTM Reader; Machine Reading System, Neural Natural Language Processing System.

Notes

Cited By

Quotes

Abstract

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.

References

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 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2015 TeachingMachinestoReadandCompreKarl Moritz Hermann
Tomas Kocisky
Edward Grefenstette
Lasse Espeholt
Will Kay
Mustafa Suleyman
Phil Blunsom
Teaching Machines to Read and Comprehend2015