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Generic and Specialized Word Embeddings for Multi-Domain Machine Translation

Authors: Pham, MinhQuang; Crego, Josep; Yvon, François; Senellart, Jean;

Generic and Specialized Word Embeddings for Multi-Domain Machine Translation

Abstract

International audience; Supervised machine translation works well when the train and test data are sampled from the same distribution. When this is not the case, adaptation techniques help ensure that the knowledge learned from out-of-domain texts generalises to in-domain sentences. We study here a related setting, multi-domain adaptation, where the number of domains is potentially large and adapting separately to each domain would waste training resources. Our proposal transposes to neural machine translation the feature expansion technique of (Daum\'e III, 2007): it isolates domain-agnostic from domain-specific lexical representations, while sharing the most of the network across domains.Our experiments use two architectures and two language pairs: they show that our approach, while simple and computationally inexpensive, outperforms several strong baselines and delivers a multi-domain system that successfully translates texts from diverse sources.

Country
France
Related Organizations
Keywords

Machine Translation, [INFO]Computer Science [cs], Domain Adaptation, [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL]

44 references, page 1 of 5

[1] K. Cho, B. van Merrienboer, D. Bahdanau, and Y. Bengio, “On the properties of neural machine translation: Encoder-decoder approaches,” in Proceedings of SSST-8, Eighth Workshop on Syntax, Semantics and Structure in Statistical Translation, Doha, Qatar, October 2014, pp. 103-111. [Online]. Available: http://www.aclweb.org/anthology/W14-4012

[2] D. Bahdanau, K. Cho, and Y. Bengio, “Neural machine translation by jointly learning to align and translate,” in Proceedings of the International Conference on Learning Representations, ser. ICLR, San Diego, CA, 2015. [Online]. Available: https: //arxiv.org/pdf/1409.0473.pdf

[3] J. Gehring, M. Auli, D. Grangier, D. Yarats, and Y. N. Dauphin, “Convolutional sequence to sequence learning,” in Proceedings of the 34th International Conference on Machine Learning, ser. Proceedings of Machine Learning Research, D. Precup and Y. W. Teh, Eds., vol. 70, International Convention Centre, Sydney, Australia, 2017, pp. 1243-1252. [Online]. Available: http://proceedings.mlr.press/v70/gehring17a.html

[4] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” in Advances in Neural Information Processing Systems 30, I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, Eds. Curran Associates, Inc., 2017, pp. 5998- 6008. [Online]. Available: http://papers.nips.cc/paper/ 7181-attention-is-all-you-need.pdf

[5] O. Firat, K. Cho, and Y. Bengio, “Multi-way, multilingual neural machine translation with a shared attention mechanism,” in Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics, 2016, pp. 866-875. [Online]. Available: http://www.aclweb.org/anthology/N16-1101

[6] T.-H. Ha, J. Niehues, and A. Waibel, “Toward multilingual neural machine translationwith universal encoder and decoder,” in Proceedings of the International Workshop on Spoken Language Translation. Vancouver, Canada: IWSLT, 2016.

[7] M. Johnson, M. Schuster, Q. Le, M. Krikun, Y. Wu, Z. Chen, N. Thorat, F. a. Vie´gas, M. Wattenberg, G. Corrado, M. Hughes, and J. Dean, “Google's multilingual neural machine translation system: Enabling zero-shot translation,” Transactions of the Association for Computational Linguistics, vol. 5, pp. 339-351, 2017. [Online]. Available: https: //transacl.org/ojs/index.php/tacl/article/view/1081

[8] G. Foster and R. Kuhn, “Mixture-model adaptation for SMT,” in Proceedings of the Second Workshop on Statistical Machine Translation, Prague, Czech Republic, 2007, pp. 128-135. [Online]. Available: http://www.aclweb.org/anthology/W/W07/W07-0717

[9] A. Axelrod, X. He, and J. Gao, “Domain adaptation via pseudo in-domain data selection,” in Proceedings of the Conference on Empirical Methods in Natural Language Processing, ser. EMNLP '11, Edinburgh, United Kingdom, 2011, pp. 355-362. [Online]. Available: http://dl.acm.org/citation.cfm?id=2145432.2145474

[10] C. Chu and R. Wang, “A survey of domain adaptation for neural machine translation,” in Proceedings of the 27th International Conference on Computational Linguistics, ser. COLING 2018, Santa Fe, New Mexico, USA, 2018, pp. 1304-1319. [Online]. Available: http://aclweb.org/anthology/C18-1111

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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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