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Question Answering is a task which requires building models capable of providing answers to questions expressed in human language.
Random search for hyper-parameter optimization
Bergstra, J. and Bengio, Y. (2012) · 2012
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Mctest: A challenge dataset for the open-domain machine comprehension of text
Richardson, M., Burges, C. J., and Renshaw, E. (2013) · 2013
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On the properties of neural machine translation: Encoder-decoder approaches
Cho, K., Van Merriënboer, B., Bahdanau, D., and Bengio, Y. (2014) · 2014
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Graves, A., Wayne, G., and Danihelka, I. (2014) · 2014
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Adam: A method for stochastic optimization
Kingma, D. and Ba, J. (2014) · 2014
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Glove: Global vectors for word representation
Pennington, J., Socher, R., and Manning, C. D. (2014) · 2014
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Sequence to sequence learning with neural networks
Sutskever, I., Vinyals, O., and Le, Q. V. (2014) · 2014
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Weston, J., Chopra, S., and Bordes, A. (2014) · 2014
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TensorFlow: Large-scale machine learning on heterogeneous systems
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., and Zheng, X. (2015) · 2015
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Teaching machines to read and comprehend
Hermann, K. M., Kocisky, T., Grefenstette, E., Espeholt, L., Kay, W., Suleyman, M., and Blunsom, P. (2015) · 2015
Cited alongside, same era.
The goldilocks principle: Reading children’s books with explicit memory representations
Hill, F., Bordes, A., Chopra, S., and Weston, J. (2015) · 2015
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Machine learning: Trends, perspectives, and prospects
Jordan, M. I. and Mitchell, T. M. (2015) · 2015
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Ask me anything: Dynamic memory networks for natural language processing
Kumar, A., Irsoy, O., Su, J., Bradbury, J., English, R., Pierce, B., Ondruska, P., Gulrajani, I., and Socher, R. (2015) · 2015
Cited alongside, same era.
Deep learning
LeCun, Y., Bengio, Y., and Hinton, G. (2015) · 2015
Cited alongside, same era.
Attention-over-attention neural networks for reading comprehension
Cui, Y., Chen, Z., Wei, S., Wang, S., Liu, T., and Hu, G. (2016) · 2016
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Hybrid computing using a neural network with dynamic external memory
Graves, A., Wayne, G., Reynolds, M., Harley, T., Danihelka, I., Grabska-Barwińska, A., Colmenarejo, S. G., Grefenstette, E., Ramalho, T., Agapiou, J., et al. (2016) · 2016
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Tracking the world state with recurrent entity networks
Henaff, M., Weston, J., Szlam, A., Bordes, A., and LeCun, Y. (2016) · 2016
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Text understanding with the attention sum reader network
Kadlec, R., Schmid, M., Bajgar, O., and Kleindienst, J. (2016) · 2016
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Dynamic entity representation with max-pooling improves machine reading
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Sukhbaatar, S., Weston, J., Fergus, R., et al. (2015) · 2015
Cited alongside, same era.
Pointer networks
Vinyals, O., Fortunato, M., and Jaitly, N. (2015) · 2015
Cited alongside, same era.
Towards ai-complete question answering: A set of prerequisite toy tasks
Weston, J., Bordes, A., Chopra, S., Rush, A. M., van Merriënboer, B., Joulin, A., and Mikolov, T. (2015) · 2015
Cited alongside, same era.
A thorough examination of the cnn/daily mail reading comprehension task
Chen, D., Bolton, J., and Manning, C. D. (2016) · 2016
Cited alongside, same era.
Kobayashi, S., Tian, R., Okazaki, N., and Inui, K. (2016) · 2016
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Who did what: A large-scale person-centered cloze dataset
Onishi, T., Wang, H., Bansal, M., Gimpel, K., and McAllester, D. (2016) · 2016
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Squad: 100,000+ questions for machine comprehension of text
Rajpurkar, P., Zhang, J., Lopyrev, K., and Liang, P. (2016) · 2016
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Natural language comprehension with the epireader
Trischler, A., Ye, Z., Yuan, X., and Suleman, K. (2016) · 2016
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Dynamic memory networks for visual and textual question answering
Xiong, C., Merity, S., and Socher, R. (2016) · 2016
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