9. References¶
- BKH16
Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E. Hinton. Layer normalization. 2016. arXiv:1607.06450.
- BCB15
Dzmitry Bahdanau, Kyung Hyun Cho, and Yoshua Bengio. Neural machine translation by jointly learning to align and translate. In 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings. International Conference on Learning Representations, ICLR, 2015. arXiv:1409.0473.
- Bak18
Amir Bakarov. A survey of word embeddings evaluation methods. CoRR, 2018. URL: http://arxiv.org/abs/1801.09536, arXiv:1801.09536.
- BGJM16
Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov. Enriching word vectors with subword information. Transactions of the Association for Computational Linguistics, 5:, 07 2016. doi:10.1162/tacl_a_00051.
- CvMG+14
Kyunghyun Cho, Bart van Merrienboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. Learning phrase representations using rnn encoder-decoder for statistical machine translation. 2014. cite arxiv:1406.1078Comment: EMNLP 2014. URL: http://arxiv.org/abs/1406.1078.
- CWB+11
Ronan Collobert, Jason Weston, Leon Bottou, Michael Karlen, Koray Kavukcuoglu, and Pavel Kuksa. Natural language processing (almost) from scratch. J. Mach. Learn. Res., 999888:2493–2537, November 2011. URL: http://dl.acm.org/citation.cfm?id=2078183.2078186.
- DDF+90
Scott Deerwester, Susan T. Dumais, George W. Furnas, Thomas K. Landauer, and Richard Harshman. Indexing by latent semantic analysis. Journal of the American Society for Information Science, 1990. doi:10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9.
- DCLT19
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. BERT: pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), 4171–4186. Minneapolis, Minnesota, June 2019. Association for Computational Linguistics. URL: https://aclanthology.org/N19-1423, doi:10.18653/v1/N19-1423.
- Fir57
J. R. Firth. A synopsis of linguistic theory 1930-55. Volume 1952-59. The Philological Society, Oxford, 1957.
- HZRS15
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. CoRR, 2015. URL: http://arxiv.org/abs/1512.03385, arXiv:1512.03385.
- JM09
Dan Jurafsky and James H. Martin. Speech and language processing : an introduction to natural language processing, computational linguistics, and speech recognition. Pearson Prentice Hall, Upper Saddle River, N.J., 2009. ISBN 9780131873216 0131873210. URL: http://www.amazon.com/Speech-Language-Processing-2nd-Edition/dp/0131873210/ref=pd_bxgy_b_img_y.
- JWT+94
Daniel Jurafsky, Chuck Wooters, Gary N. Tajchman, Jonathan Segal, Andreas Stolcke, Eric Fosler, and Nelson Morgan. The berkeley restaurant project. In The 3rd International Conference on Spoken Language Processing, ICSLP 1994, Yokohama, Japan, September 18-22, 1994. ISCA, 1994. URL: http://www.isca-speech.org/archive/icslp\_1994/i94\_2139.html.
- MS00
Christopher D Manning and Hinrich Schuetze. Foundations of Natural Language Processing. Reading, 2000.
- MSC+13
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. Distributed representations of words and phrases and their compositionality. In C. J. C. Burges, L. Bottou, M. Welling, Z. Ghahramani, and K. Q. Weinberger, editors, Advances in Neural Information Processing Systems 26, pages 3111–3119. Curran Associates, Inc., 2013. URL: http://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf.
- PSM14
Jeffrey Pennington, Richard Socher, and Christopher D. Manning. Glove: global vectors for word representation. In Empirical Methods in Natural Language Processing (EMNLP), 1532–1543. 2014. URL: http://www.aclweb.org/anthology/D14-1162.
- RE16
Colin Raffel and Daniel P W Ellis. Feed-forward networks with attention can solve some long-term memory problems. In Workshop track-ICLR 2016 FEED-FORWARD NETWORKS WITH ATTENTION CAN SOLVE SOME LONG-TERM MEMORY PROBLEMS.pdf:pdf. 2016. arXiv:1512.08756v5.
- SVL14
Ilya Sutskever, Oriol Vinyals, and Quoc V. Le. Sequence to sequence learning with neural networks. In Advances in Neural Information Processing Systems, volume 4, 3104–3112. Neural information processing systems foundation, 2014. arXiv:1409.3215.
- VSP+17
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is All you Need. In Isabelle Guyon, Ulrike von Luxburg, Samy Bengio, Hanna M Wallach, Rob Fergus, S V N Vishwanathan, and Roman Garnett, editors, Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 4-9 December 2017, Long Beach, CA, \USA\, 6000–6010. 2017. URL: http://papers.nips.cc/paper/7181-attention-is-all-you-need.