publication . Preprint . 2018

Multilingual Neural Machine Translation with Task-Specific Attention

Blackwood, Graeme; Ballesteros, Miguel; Ward, Todd;
Open Access English
  • Published: 08 Jun 2018
Abstract
Multilingual machine translation addresses the task of translating between multiple source and target languages. We propose task-specific attention models, a simple but effective technique for improving the quality of sequence-to-sequence neural multilingual translation. Our approach seeks to retain as much of the parameter sharing generalization of NMT models as possible, while still allowing for language-specific specialization of the attention model to a particular language-pair or task. Our experiments on four languages of the Europarl corpus show that using a target-specific model of attention provides consistent gains in translation quality for all possibl...
Subjects
free text keywords: Computer Science - Computation and Language
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24 references, page 1 of 2

[Ammar et al.2016] Waleed Ammar, George Mulcaire, Miguel Ballesteros, Chris Dyer, and Noah A. Smith. 2016. Many languages, one parser. TACL, 4:431-444.

[Bahdanau et al.2014] Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014. Neural machine translation by jointly learning to align and translate. CoRR, abs/1409.0473.

[Baxter2000] Jonathan Baxter. 2000. A model of inductive bias learning. J. Artif. Intell. Res., 12:149-198. [OpenAIRE]

[Caruana1997] Rich Caruana. 1997. Multitask learning. Machine Learning, 28(1):41-75.

[Cho et al.2014] KyungHyun Cho, Bart van Merrienboer, Dzmitry Bahdanau, and Yoshua Bengio. 2014. On the properties of neural machine translation: Encoder-decoder approaches. CoRR, abs/1409.1259.

[Dong et al.2015] Daxiang Dong, Hua Wu, Wei He, Dianhai Yu, and Haifeng Wang. 2015. Multi-task learning for multiple language translation. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 1723-1732, Beijing, China, July. Association for Computational Linguistics.

[Johnson et al.2017] Melvin Johnson, Mike Schuster, Quoc Le, Maxim Krikun, Yonghui Wu, Zhifeng Chen, Nikhil Thorat, Fernand a Vigas, Martin Wattenberg, Greg Corrado, Macduff Hughes, and Jeffrey Dean. 2017. Google's multilingual neural machine translation system: Enabling zero-shot translation. Transactions of the Association for Computational Linguistics, 5:339-351.

[Kann et al.2018] Katharina Kann, Jesus Manuel Mager Hois, Ivan Vladimir Meza Ruiz, and Hinrich Schu¨tze. 2018. Fortification of neural morphological segmentation models for polysynthetic minimal-resource languages. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 47-57. Association for Computational Linguistics.

[Kingma and Ba2014] Diederik P. Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. CoRR, abs/1412.6980.

[Kiperwasser and Ballesteros2018] Eliyahu Kiperwasser and Miguel Ballesteros. 2018. Scheduled multi-task learning: From syntax to translation. TACL, 6:225-240.

[Koehn and Knowles2017] Philipp Koehn and Rebecca Knowles. 2017. Six challenges for neural machine translation. CoRR, abs/1706.03872.

[Koehn et al.2007] P. Koehn, H. Hoang, A. Birch, C. Callison-Burch, M. Federico, N. Bertoldi, B. Cowan, W. Shen, C. Moran, R. Zens, C. Dyer, O. Bojar, A. Constantin, and E. Herbst. 2007. Moses: Open source toolkit for statistical machine translation. In Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics.

[Matusov et al.2006] E. Matusov, N. Ueffing, and H. Ney. 2006. Computing consensus translation from multiple machine translation systems using enhanced hypotheses alignment. In Proceedings of the European Association for Computational Linguistics.

[Neubig et al.2017a] Graham Neubig, Chris Dyer, Yoav Goldberg, Austin Matthews, Waleed Ammar, Antonios Anastasopoulos, Miguel Ballesteros, David Chiang, Daniel Clothiaux, Trevor Cohn, Kevin Duh, Manaal Faruqui, Cynthia Gan, Dan Garrette, Yangfeng Ji, Lingpeng Kong, Adhiguna Kuncoro, Gaurav Kumar, Chaitanya Malaviya, Paul Michel, Yusuke Oda, Matthew Richardson, Naomi Saphra, Swabha Swayamdipta, and Pengcheng Yin. 2017a. Dynet: The dynamic neural network toolkit. arXiv preprint arXiv:1701.03980.

[Neubig et al.2017b] Graham Neubig, Yoav Goldberg, and Chris Dyer. 2017b. On-the-fly operation batching in dynamic computation graphs. In Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 4-9 December 2017, Long Beach, CA, USA, pages 3974-3984.

24 references, page 1 of 2
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