Abstract
With the globalization and the rapid development of the Internet, machine translation is becoming more widely used in real world applications. However, existing methods are not good enough for domain adaptation translation. Consequently, we may understand the cutting-edge techniques in a field better and faster with the aid of our machine translation. This paper proposes a method of calculating the balance factor based on model fusion algorithm and logarithmic linear interpolation. A neural machine translation technique is used to train a domain adaptation translation model. In our experiments, the BLEU score of the in-domain corpus reaches 43.55, which shows a certain increase when comparing to existing methods.
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Brown PF, Cocke J, Pietra SAD et al (2002) A statistical approach to machine translation. Comput Linguist 16(2):79–85
Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Comput Linguist 29(1):19–51
Eisner J (2003) Learning non-isomorphic tree mappings for machine translation. In: ACL 2003, meeting of the association for computational linguistics, companion volume to the proceedings, 7–12 July 2003. Sapporo Convention Center, Sapporo, Japan, pp 205–208
Kalchbrenner N, Blunsom P (2013) Recurrent continuous translation models
Sutskever I, Vinyals O, Le QV (2014) Sequence to sequence learning with neural networks. Adv Neural Inf Process Syst 4:3104–3112
Bahdanau D, Cho K, Bengio Y (2014) Neural machine translation by jointly learning to align and translate. Comput Sci
Freitag M, Alonaizan Y (2016) Fast domain adaptation for neural machine translation. arXiv preprint. arXiv:1612.06897
Sennrich R, Haddow B, Birch A (2016) Controlling politeness in neural machine translation via side constraints. In: Conference of the North American chapter of the association for computational linguistics: human language technologies, pp 35–40
Chu C, Dabre R, Kurohashi S (2017) An empirical comparison of simple domain adaptation methods for neural machine translation. arXiv preprint. arXiv:1701.03214
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Tian, X., Liu, J., Pu, J., Wang, J. (2019). A Domain Adaptation Method for Neural Machine Translation. In: Park, J., Loia, V., Choo, KK., Yi, G. (eds) Advanced Multimedia and Ubiquitous Engineering. MUE FutureTech 2018 2018. Lecture Notes in Electrical Engineering, vol 518. Springer, Singapore. https://doi.org/10.1007/978-981-13-1328-8_41
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DOI: https://doi.org/10.1007/978-981-13-1328-8_41
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