Performance comparison of neural machinetranslation systems in Uyghur-Chinese translation
Halidanmu Abudukelimu, LIU Yang, SUN Maosong
Tsinghua National Laboratory for Information Science and Technology, State Key Laboratory of Intelligent Technology and Systems, Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China
Abstract：The neural machine translation based on deep learning significantly surpasses the traditional statistical machine translation in many languages, and becomes the current mainstream machine translation technology. This paper compares six influential neural machine translation methods from the level of word granularity in the task of Uyghur-Chinese machine translation. These methods are attention mechanism (GroundHog), vocabulary expansion (LV-groundhog), source language and target language with subword units (subword-nmt), characters and words mixed (nmt.hybrid), subword units and characters (dl4mt-cdec), and complete characters (dl4mt-c2c). The experimental results show that Uyghur-Chinese neural machine translation performs best when the source language is segmented into subword units and the target language is represented by characters (dl4mt-cdec). This paper is the first to use neural machine translation for Uyghur-Chinese machine translation and the first to compare different neural machine translation methods on the same corpus. This work is an important reference not only for Uyghur-Chinese machine translation, but also for general neural machine translation tasks.
哈里旦木·阿布都克里木, 刘洋, 孙茂松. 神经机器翻译系统在维吾尔语-汉语翻译中的性能对比[J]. 清华大学学报（自然科学版）, 2017, 57(8): 878-883.
Halidanmu Abudukelimu, LIU Yang, SUN Maosong. Performance comparison of neural machinetranslation systems in Uyghur-Chinese translation. Journal of Tsinghua University(Science and Technology), 2017, 57(8): 878-883.
Sutskever I, Vinyals O, Le Q. Sequence to Sequence Learning with Neural Networks[Z/OL]. (2014-09-10)[2015-03-15]. https://arxiv.org/abs/1409.3215.
[2]
Bahdanau D, Cho K, Bengio Y. Neural Machine Translation by Jointly Learning to Align and Translate[Z/OL]. (2014-09-01)[2015-03-20]. https://arxiv.org/abs/1409.0473.
[3]
Wu Y, Schuster M, Chen Z, et al. Google's Neural Machine Translation System:Bridging the Gap between Human and Machine Translation[Z/OL]. (2016-09-26)[2016-10-02]. https://arxiv.org/abs/1609.08144.
[4]
Johnson M, Schuster M, Le Q, et al. Google's Multilingual Neural Machine Translation System:Enabling Zero-Shot Translation[Z/OL]. (2016-11-14)[2016-11-16]. https://arxiv.org/abs/1611.04558.
[5]
Jean S, Cho K, Memisevic R, et al. On using very large target vocabulary for neural machine translation[C]//Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics. Beijing, 2015:1-10.
[6]
Luong M, Sutskever I, Le Q, et al. Addressing the rare word problem in neural machine translation[C]//Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics. Beijing, 2015:11-19.
[7]
Chung J, Cho K, Bengio Y. A character-level decoder without explicit segmentation for neural machine translation[C]//Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics. Berlin, Germany, 2016:1693-1703.
[8]
Ling W, Trancoso I, Dyer C, et al. Character-Based Neural Machine Translation[Z/OL]. (2015-11-14)[2016-02-03]. https://arxiv.org/abs/1511.04586.
[9]
Costa-Jussà M, Fonollosa J. Character-based neural machine translation[C]//Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics. Berlin, Germany, 2016:357-361.
[10]
Luong M, Manning C. Achieving open vocabulary neural machine translation with hybrid word-character models[C]//Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics. Berlin, Germany, 2016:1054-1063.
[11]
Lee J, Cho K, Hofmann T. Fully Character-Level Neural Machine Translation without Explicit Segmentation[Z/OL]. (2016-10-10)[2016-10-22]. https://arxiv.org/abs/1610.03017.
[12]
Sennrich R, Haddow B, Birch A. Neural machine translation of rare words with subword units[C]//Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics. Berlin, Germany, 2016:1715-1725.
[13]
Luong M, Le Q, Sutskever I, et al. Multi-Task Sequence to Sequence Learning[Z/OL]. (2015-11-19)[2016-07-17]. https://arxiv.org/abs/1511.06114.
[14]
Firat O, Cho K, Bengio Y. Multi-way, multilingual neural machine translation with a shared attention mechanism[C]//Proceedings of NAACL-HLT. San Diego, CA, USA, 2016:866-875.
[15]
Firat O, Sankaran B, Al-Onaizan Y, et al. Zero-resource translation with multi-lingual neural machine translation[C]//Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. Austin, TX, USA, 2016:268-277.
[16]
哈里旦木·阿布都克里木, 程勇, 刘洋, 等. 基于双向门限递归单元神经网络的维吾尔语形态切分[J]. 清华大学学报(自然科学版), 2017, 57(1):1-6. ABUDUKELIMU Halidanmu, CHENG Yong, LIU Yang, et al. Uyghur morphological segmentation with bidirectional GRU neural networks[J]. J Tsinghua Univ (Sci and Tech), 2017, 57(1):1-6. (in Chinese)
[17]
Abudukelimu H, Liu Y, Chen X, et al. Learning distributed representations of Uyghur words and morphemes[C]//Proceedings of CCL/NLP-NABD. Guangzhou, 2015:202-211.
[18]
Creutz M, Lagus K. Unsupervised discovery of morphemes[C]//Morphological and Phonological Learning:Proceedings of the 6th Workshop of the ACL Special Interest Group in Computational Phonology (SIGPHON). Stroudsburg, PA, USA, 2002:21-30.
[19]
Koehn P, Hoang H, Birch A, et al. Moses:Open source toolkit for statistical machine translation[C]//Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Pargue, Czech Republic, 2007:177-180.
[20]
Sennrich R. How Grammatical Is Character-Level Neural Machine Translation Assessing MT Quality with Contrastive Translation Pairs[Z/OL]. (2016-12-14)[2016-12-20]. https://arxiv.org/abs/1612.04629.
[21]
Zohp B, Yuret D, May J, et al. Transfer learning for low-resource neural machine translation[C]//Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. Austin, TX, 2016:1568-1575.
[22]
Luong M, Pham H, Manning C. Effective approaches to attention-based neural machine translation[C]//Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. Lisbon, Portugal, 2015:1412-1421.
2017, Vol. 57 
