Towards Achieving Machine Comprehension Using Deep Learning on Non-GPU Machines

U. Khan, K. Khan, F. Hassan, A. Siddiqui, M. Afaq

Abstract


Long efforts have been made to enable machines to understand human language. Nowadays such activities fall under the broad umbrella of machine comprehension. The results are optimistic due to the recent advancements in the field of machine learning. Deep learning promises to bring even better results but requires expensive and resource hungry hardware. In this paper, we demonstrate the use of deep learning in the context of machine comprehension by using non-GPU machines. Our results suggest that the good algorithm insight and detailed understanding of the dataset can help in getting meaningful results through deep learning even on non-GPU machines.


Keywords


natural language processing; machine comprehension; deep learning; non-GPU machines; SQuAD

Full Text:

PDF

References


D. Karunakaran, “Entity extraction using Deep Learning based on Guillaume Genthial work on NER”, available at: https://

medium.com/intro-to-artificial-intelligence/entity-extraction-using-deep-learning-8014acac6bb8, 2017

https://rajpurkar.github.io/SQuAD-explorer/

P. Rajpurkar, J. Zhang, K. Lopyrev, P. Liang, “SQuAD: 100,000+ Questions for Machine Comprehension of Text”, available at: https://arxiv.org/abs/1606.05250, 2016

D. Chen, A. Fisch, J. Weston, A. Bordes, “Reading Wikipedia to Answer Open-Domain Questions”, 55th Annual Meeting of the Association for Computational Linguistics, Vancouver, Canada, July 30-August 4, 2017

E. Loper, S. Bird, “NLTK: The natural language toolkit”, ACL-02 Workshop on Effective tools and Methodologies for Teaching Natural Language Processing and Computational Linguistics, Philadelphia, USA, July 7, 2002

M. Richardson, C. J. C. Burges, E. Renshaw, “MCTest: A Challenge Dataset for the Open-Domain Machine Comprehension of Text”, Conference on Empirical Methods in Natural Language Processing, Washington, USA, October 18-21, 2013

J. Weston, A. Bordes, S. Chopra, A. M. Rush, B. V. Merrienboer, A. Joulin, T. Mikolov, “Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks”, 4rth International Conference on Learning Representations, New York, USA, May 2-4, 2016

K. M. Hermann, T. Kocisky, E. Grefenstette, L. Espeholt, W. Kay, M. Suleyman, P. Blunsom, “Teaching Machines to Read and Comprehend”, International Conference on Neural Information Processing Systems, Montreal, Canada, December 7-12, 2015

S. B. Kotsiantis, “Supervised machine learning: A review of classification techniques”, Informatica, Vol. 31, pp. 249-268, 2007

J. Schmidhuber, “Deep learning in neural networks: An overview”, Neural Networks, Vol. 61, pp. 85-117, 2015

B. F. Green Jr, A. K. Wolf, C. Chomsky, K. Laughery, “Baseball: An Automatic Question-Answerer”, Western Joint IRE-AIEE-ACM Computer Conference , Los Angeles, California, May 9-11, 1961

M. Seo, A. Kembhavi, A. Farhadi, H. Hajishirzi, “Bidirectional Attention Flow for Machine Comprehension”, 5th International Conference on Learning Representations, Toulon, France, April 24-26, 2017

Y. LeCun, Y. Bengio, G. Hinton, “Deep Learning”, Nature, Vol. 521, Article ID 7553, 2015

K. Greff, R. K. Srivastava, J. Koutnik, B. R. Steunebrink, J. Schmidhuber, “LSTM: A search space odyssey”, Transactions on Neural Networks and Learning Systems, Vol. 28, No. 10, pp. 2222-2232, 2017

L. Yu, K. M. Hermann, P. Blundom, S. Pulman, “Deep learning for answer sentence selection”, available at: https://arxiv.org/pdf/

1632.pdf, 2014

A. Finch, Y. S. Hwang, E. Sumita, “Using machine translation evaluation techniques to determine sentence-level semantic equivalence”, Third International Workshop on Paraphrasing, Jeju Island, Korea October 14, 2005

K. Cho, B. V. Merrienboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, Y. Bengio, “Learning Phrase Representations Using RNN Encoder-Decoder for Statistical Machine Translation”, Conference on Empirical Methods in Natural Language Processing, Doha, Qatar, October 25-29, 2014

J, Pennington, R. Socher, C. D. Manning, “GloVe: Global Vectors for Word Representation”, Conference on Empirical Methods in Natural Language Processing Doha, Qatar, October 25-29, 2014




eISSN: 1792-8036     pISSN: 2241-4487