Data augmentation for low-Resource neural machine translation

ACL 2017 - 55th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers)

Volumn 2, Issue , 2017, Pages 567-573

  Fadaee, Marzieh ^a   Bisazza, Arianna ^a   Monz, Christof ^a  

^a UNIVERSITY OF AMSTERDAM   (Netherlands)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL LINGUISTICS; COMPUTER AIDED LANGUAGE TRANSLATION; LINGUISTICS;

BACK TRANSLATIONS; DATA AUGMENTATION; LOW RESOURCE LANGUAGES; LOW-RESOURCE SETTINGS; MACHINE TRANSLATION SYSTEMS; MACHINE TRANSLATIONS; PARALLEL CORPORA; TRANSLATION QUALITY;

TRANSLATION (LANGUAGES);

EID: 85037344824     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.18653/v1/P17-2090     Document Type: Conference Paper

References (16)

1. Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2015. Neural machine translation by jointly learning to align and translate. In Proceedings of the International Conference on Learning Representations (ICLR).
2. Ondrej Bojar, Rajen Chatterjee, Christian Feder-mann, Yvette Graham, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, Varvara Logacheva, Christof Monz, Matteo Negri, Aurelie Neveol, Mariana Neves, Martin Popel, Matt Post, Raphael Rubino, Carolina Scarton, Lucia Specia, Marco Turchi, Karin Verspoor, and Marcos Zampieri. 2016. Findings of the 2016 conference on machine translation. In Proceedings of the First Conference on Machine Translation. Association for Computational Linguistics, Berlin, Germany, pages 131–198. http://www.aclweb.org/anthology/W/W16/W162301.
3. Ken Chatfield, Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman. 2014. Return of the devil in the details: Delving deep into convolutional nets. In Proceedings of the British Machine Vision Conference. BMVA Press. https://doi.org/http://dx.doi.org/10.5244/C.28.6.
4. Kyunghyun Cho, B van Merrienboer, Dzmitry Bahdanau, and Yoshua Bengio. 2014. On the properties of neural machine translation: Encoder-decoder approaches. In Eighth Workshop on Syntax, Semantics and Structure in Statistical Translation (SSST-8), 2014.
5. Long Duong, Trevor Cohn, Steven Bird, and Paul Cook. 2015. A neural network model for low-resource universal dependency parsing. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, Lisbon, Portugal, pages 339–348. http://aclweb.org/anthology/D15-1040.
6. Chris Dyer, Victor Chahuneau, and Noah A. Smith. 2013. A simple, fast, and effective reparameterization of ibm model 2. In Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics, Atlanta, Georgia, pages 644–648. http://www.aclweb.org/anthology/N13-1073.
7. Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural Computation 9(8):1735–1780.
8. Rafal Jozefowicz, Wojciech Zaremba, and Ilya Sutskever. 2015. An empirical exploration of recurrent network architectures. In Francis Bach and David Blei, editors, Proceedings of the 32nd International Conference on Machine Learning. PMLR, Lille, France, volume 37 of Proceedings of Machine Learning Research, pages 2342–2350. http://proceedings.mlr.press/v37/jozefowicz15.pdf.
9. Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. 2012. Imagenet classification with deep convolutional neural networks. In F. Pereira, C. J. C. Burges, L. Bottou, and K. Q. Weinberger, editors, Advances in Neural Information Processing Systems 25, Curran Associates, Inc., pages 1097–1105. http://papers.nips.cc/paper/4824imagenet-classification-with-deep-convolutional-neural-networks.pdf.
10. Thang Luong, Hieu Pham, and Christopher D. Manning. 2015. Effective approaches to attention-based neural machine translation. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, Lisbon, Portugal, pages 1412–1421. http://aclweb.org/anthology/D15-1166.
11. Yuval Marton, Chris Callison-Burch, and Philip Resnik. 2009. Improved statistical machine translation using monolingually-derived paraphrases. In Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, Singapore, pages 381–390. http://www.aclweb.org/anthology/D/D09/D091040.
12. Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002. Bleu: a method for automatic evaluation of machine translation. In Proceedings of 40th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, Philadelphia, Pennsylvania, USA, pages 311–318. https://doi.org/10.3115/1073083.1073135.
13. Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016a. Improving neural machine translation models with monolingual data. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, Berlin, Germany, pages 86–96. http://www.aclweb.org/anthology/P16-1009.
14. Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016b. Neural machine translation of rare words with subword units. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, Berlin, Germany, pages 1715–1725. http://www.aclweb.org/anthology/P16-1162.
15. Ilya Sutskever, Oriol Vinyals, and Quoc V Le. 2014. Sequence to sequence learning with neural networks. In Z. Ghahramani, M. Welling, C. Cortes, N. D. Lawrence, and K. Q. Weinberger, editors, Advances in Neural Information Processing Systems 27, Curran Associates, Inc., pages 3104–3112. http://papers.nips.cc/paper/5346-sequenceto-sequence-learning-with-neural-networks.pdf.
16. Barret Zoph, Deniz Yuret, Jonathan May, and Kevin Knight. 2016. Transfer learning for low-resource neural machine translation. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, Austin, Texas, pages 1568–1575. https://aclweb.org/anthology/D16-1163.