References(50)
[1]
I. Sutskever, O. Vinyals, and V. Le Quoc, Sequence to sequence learning with neural networks, in Proc. 27th Int. Conf. Neural Information Processing Systems, Cambridge, MA, USA, 2014, pp. 3104-3112.
[2]
D. Bahdanau, K. Cho, and Y. Bengio, Neural machine translation by jointly learning to align and translate, in Proc. 3rd Int. Conf. Learning Representations, San Diego, CA, USA, 2015.
[3]
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł Kaiser, and I. Polosukhin, Attention is all you need, in Proc. 31st Int. Conf. Neural Information Processing Systems, Red Hook, NY, USA, 2017, pp. 6000-6010.
[4]
Y. H. Wu, M. Schuster, Z. F. Chen, V. Le Quoc, M. Norouzi, W. Macherey, M. Krikun, Y. Cao, Q. Gao, K. Macherey, et al., Google’s neural machine translation system: Bridging the gap between human and machine translation, arXiv preprint arXiv: 1609.08144, 2016.
[5]
S. Hochreiter and J. Schmidhuber, Long short-term memory, Neural Computation, vol. 9, no. 8, pp. 1735-1780, 1997.
[6]
K. Cho, B. van Merriënboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, and Y. Bengio, Learning phrase representations using RNN encoder-decoder for statistical machine translation, in Proc. 2014 Conf. Empirical Methods in Natural Language Processing, Doha, Qatar, 2014, pp. 1724-1734.
[7]
J. Devlin, M. W. Chang, K. Lee, and K. Toutanova, BERT: Pre-training of deep bidirectional transformers for language understanding, arXiv preprint arXiv: 1810.04805, 2018.
[8]
P. F. Brown, V. J. Della Pietra, S. A. Della Pietra, and R. L. Mercer, The mathematics of statistical machine translation: Parameter estimation, Computational Linguistics, vol. 19, no. 2, pp. 263-311, 1993.
[9]
P. Koehn, F. J. Och, and D. Marcu, Statistical phrase-based translation, in Proc. 2003 Conf. North American Chapter of the Association for Computational Linguistics on Human Language Technology, Stroudsburg, PA, USA, 2003, pp. 48-54.
[10]
D. Chiang, A hierarchical phrase-based model for statistical machine translation, in Proc. 43rd Annu. Meeting on Association for Computational Linguistics, Stroudsburg, PA, USA, 2005, pp. 263-270.
[11]
M. Junczys-Dowmunt, T. Dwojak, and H. Hoang, Is neural machine translation ready for deployment? A case study on 30 translation directions, arXiv preprint arXiv: 1610.01108, 2016.
[12]
B. Zoph, D. Yuret, J. May, and K. Knight, Transfer learning for low-resource neural machine translation, in Proc. 2016 Conf. Empirical Methods in Natural Language Processing, Austin, Texas, 2016, pp. 1568-1575.
[13]
C. Vania and A. Lopez, From characters to words to in between: Do we capture morphology? in Proc. 55th Annu. Meeting of the Association for Computational Linguistics, Vancouver, Canada, 2017, pp. 2016-2027.
[14]
Y. Chen, Y. Liu, and V. O. K. Li, Zero-resource neural machine translation with multi-agent communication game, arXiv preprint arXiv: 1802.03116, 2018.
[15]
A. Karakanta, J. Dehdari, and J. van Genabith, Neural machine translation for low-resource languages without parallel corpora, Machine Translation, vol. 32, nos. 1&2, pp. 167-189, 2018.
[17]
Y. Chen, Y. Liu, Y. Cheng, and V. O. L. Li, A teacher-student framework for zero-resource neural machine translation, in Proc. 55th Annu. Meeting of the Association for Computational Linguistics, Vancouver, Canada, 2017, pp. 1925-1935.
[18]
H. Zheng, Y. Cheng, and Y. Liu, Maximum expected likelihood estimation for zero-resource neural machine translation, in Proc. 26th Int. Joint Conf. Artificial Intelligence, Melbourne, Australia, 2017, pp. 4251-4257.
[19]
Y. Cheng, W. Xu, Z. J. He, W. He, H. Wu, M. S. Sun, and Y. Liu, Semi-supervised learning for neural machine translation, in Proc. 54th Annu. Meeting of the Association for Computational Linguistics, Berlin, Germany, 2016, pp. 1965-1974.
[20]
Y. Cheng, Q. Yang, Y. Liu, M. S. Sun, and W. Xu, Joint training for pivot-based neural machine translation, in Proc. 26th Int. Joint Conf. Artificial Intelligence, Melbourne, Australia, 2017, pp. 3974-3980.
[21]
P. Passban, Q. Liu, and A. Way, Translating low-resource languages by vocabulary adaptation from close counterparts, ACM Transactions on Asian and Low-Resource Language Information Processing, vol. 16, no. 4, p. 29, 2017.
[22]
M. Maimaiti and X. H. Zou, Discussion on bilingual cognition in international exchange activities, presented at International Conference on Intelligence Science (ICIS2018), Beijing, China, 2018, pp. 167-177.
[23]
T. Kocmi and O. Bojar, Trivial transfer learning for low-resource neural machine translation, in Proc. 3rd Conf. Machine Translation: Research Papers, Brussels, Belgium, 2018, pp. 244-252.
[25]
Y. Kim, Y. B. Gao, and H. Ney, Effective cross-lingual transfer of neural machine translation models without shared vocabularies, in Proc. 57th Annu. Meeting of the Association for Computational Linguistics, Florence, Italy, 2019, pp. 1246-1257.
[26]
M. Johnson, M. Schuster, V. Le Quoc, M. Krikun, Y. H. Wu, Z. F. Chen, N. Thorat, F. Viégas, M. Wattenberg, G. Corrado, et al., Google’s multilingual neural machine translation system: Enabling zero-shot translation, Transactions of the Association of Computational Linguistics, vol. 5, pp. 339-351, 2017.
[27]
S. J. Pan and Q. Yang, A survey on transfer learning, IEEE Transactions on Knowledge and Data Engineering, vol. 22, no. 10, pp. 1345-1359, 2010.
[28]
S. Hong, J. Oh, H. Lee, and B. Han, Learning transferrable knowledge for semantic segmentation with deep convolutional neural network, presented 2016 IEEE Conf. Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 2016, pp. 3204-3212.
[29]
M. Oquab, L. Bottou, I. Laptev, and J. Sivic, Learning and transferring mid-level image representations using convolutional neural networks, presented at 2014 IEEE Conf. Computer Vision and Pattern Recognition, Columbus, OH, USA, 2014, pp. 1717-1724.
[30]
B. Tan, Y. Zhang, S. J. Pan, and Q. Yang, Distant domain transfer learning, in Proc. 31st AAAI Conf. Artificial Intelligence, San Francisco, CA, USA, 2017, pp. 2604-2610.
[31]
K. M. He, X. Y. Zhang, S. Q. Ren, and J. Sun, Deep residual learning for image recognition, in Proc. 2016 IEEE Conf. Computer Vision and Pattern Recognition, Las Vegas, NV, USA, 2016, pp. 770-778.
[32]
J. L. Ba, J. R. Kiros, and G. E. Hinton, Layer normalization, arXiv preprint arXiv: 1607.06450, 2016.
[33]
Z. G. Li and M. S. Sun, Punctuation as implicit annotations for Chinese word segmentation, Computational Linguistics, vol. 35, no. 4, pp. 505-512, 2009.
[34]
T. Luong, I. Sutskever, V. Le Quoc, O. Vinyals, and W. Zaremba, Addressing the rare word problem in neural machine translation, in Proc. 53rd Annu. Meeting of the Association for Computational Linguistics and the 7th Int. Joint Conf. Natural Language Processing, Beijing, China, 2015, pp. 11-19.
[35]
R. Sennrich, B. Haddow, and A. Birch, Neural machine translation of rare words with subword units, arXiv preprint arXiv: 1508.07909, 2016.
[36]
J. C. Zhang, Y. Z. Ding, S. Q. Shen, Y. Cheng, M. S. Sun, H. B. Luan, and Y. Liu, THUMT: An open source toolkit for neural machine translation, arXiv preprint arXiv: 1706.06415, 2017.
[37]
K. Papineni, S. Roukos, T. Ward, and W. J. Zhu, Bleu: A method for automatic evaluation of machine translation, in Proc. 40th Annu. Meeting of the Association for Computational Linguistics, Philadelphia, PA, USA, 2002, pp. 311-318.
[38]
D. X. Dong, H. Wu, W. He, D. H. Yu, and H. F. Wang, Multi-task learning for multiple language translation, in Proc. 53rd Annu. Meeting of the Association for Computational Linguistics and the 7th Int. Joint Conf. Natural Language Processing, Beijing, China, 2015, pp. 1723-1732.
[39]
O. Firat, B. Sankaran, Y. Al-Onaizan, F. T. Y. Vural, and K. Cho, Zero-resource translation with multi-lingual neural machine translation, in Proc. 2016 Conf. Empirical Methods in Natural Language Processing, Austin, TX, USA, 2016, pp. 268-277.
[40]
O. Firat, K. Cho, and Y. Bengio, Multi-way, multilingual neural machine translation with a shared attention mechanism, in Proc. 2016 Conf. North American Chapter of the Association for Computational Linguistics: Human Language Technologies, San Diego, CA, USA, 2016, pp. 866-875.
[41]
B. Zoph, V. Vasudevan, J. Shlens, and V. Le Quoc, Learning transferable architectures for scalable image recognition, presented at 2018 IEEE/CVF Conf. Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 2018, pp. 8697-8710.
[42]
C. H. Chu, R. Dabre, and S. Kurohashi, An empirical comparison of simple domain adaptation methods for neural machine translation, arXiv preprint arXiv: 1701.03214, 2017.
[43]
J. L. Zeng, J. S. Su, H. T. Wen, Y. Liu, J. Xie, Y. J. Yin, and J. Q. Zhao, Multi-domain neural machine translation with word-level domain context discrimination, in Proc. 2018 Conf. Empirical Methods in Natural Language Processing, Brussels, Belgium, 2018, pp. 447-457.
[44]
J. L. Zeng, Y. Liu, J. S. Su, Y. B. Ge, Y. J. Lu, Y. J. Yin, and J. B. Luo, Iterative dual domain adaptation for neural machine translation, in Proc. 2019 Conf. Empirical Methods in Natural Language Processing and the 9th Int. Joint Conf. Natural Language Processing, Hong Kong, China, 2019, pp. 845-855.
[45]
J. S. Su, J. L. Zeng, J. Xie, H. T. Wen, Y. J. Yin, and Y. Liu, Exploring discriminative word-level domain contexts for multi-domain neural machine translation, IEEE Transactions on Pattern Analysis and Machine Intelligence, .
[46]
F. Marzieh, A. Bisazza, and C. Monz, Data augmentation for low-resource neural machine translation, in Proc. 55th Annu. Meeting of the Association for Computational Linguistics, Vancouver, Canada, 2017, pp. 567-573.
[47]
J. T. Gu, Y. Wang, Y. Chen, V. O. K. Li, and K. Cho, Meta-learning for low-resource neural machine translation, in Proc. 2018 Conf. Empirical Methods in Natural Language Processing, Brussels, Belgium, 2018, pp. 3622-3631.
[48]
H. Setiawan, Z. Q. Huang, and R. Zbib, BBN’s low-resource machine translation for the LoReHLT 2016 evaluation, Machine Translation, vol. 32, no. 1, pp. 45-57, 2018.
[49]
M. Maimaiti, Y. Liu, H. B. Luan, and M. S. Sun, Multi-round transfer learning for low-resource NMT using multiple high-resource languages, ACM Transactions on Asian and Low-Resource Language Information Processing, vol. 18, no. 4, p. 38, 2019.
[50]
T. Q. Nguyen and D. Chiang, Transfer learning across low-resource, related languages for neural machine translation, in Proc. 8th Int. Joint Conf. Natural Language Processing, Taipei, China, 2017. pp. 296-301.