|
[1]
|
Bahdanau, D., Cho, K. and Bengio, Y. (2014) Neural Machine Translation by Jointly Learning to Align and Translate.
https://arxiv.org/abs/1409.0473
|
|
[2]
|
Luong, M.T., Pham, H. and Manning, C.D. (2015) Effective Approaches to Attentionbased Neural Machine Translation. Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, Lisbon, 17-21 September 2015, 1412-1421. [Google Scholar] [CrossRef]
|
|
[3]
|
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L. and Polosukhin, I. (2017) Attention Is All You Need. 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, 4-9 December 2017, 5998-6008.
|
|
[4]
|
李洪政, 冯冲, 黄河燕. 稀缺资源语言神经网络机器翻译研究综述[J]. 自动化学报, 2021, 47(6): 1217-1231.
|
|
[5]
|
Marlies, W., Bisazza, A. and Monz, C. (2017) Dynamic Data Selection for Neural Machine Translation. Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (EMNLP), Copenhagen, 7-11 September 2017, 1400-1410.
|
|
[6]
|
Zhang, P., Xu, X. and Xiong, D. (2018) Active Learning for Neural Machine Translation. 2018 International Conference on Asian Language Processing, Bandung, 15-17 November 2018, 153-158. [Google Scholar] [CrossRef]
|
|
[7]
|
Wang, R., Utiyama, M., Finch, A.M., Liu, L., Chen, K. and Sumita, E. (2018) Sentence Selection and Weighting for Neural Machine Translation Domain Adaptation. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 26, 1727-1741. [Google Scholar] [CrossRef]
|
|
[8]
|
宜年, 艾山∙吾买尔, 买合木提∙买买提, 等. 基于多种数据筛选的维汉神经机器翻译[J]. 厦门大学学报(自然科学版), 2022, 61(4): 660-666.
|
|
[9]
|
刘欢, 刘俊鹏, 黄锴宇, 等. 面向低资源俄汉机器翻译的领域适应方法[J]. 厦门大学学报(自然科学版), 2022, 61(4): 654-659.
|
|
[10]
|
Sennrich, R., Haddow, B. and Birch, A. (2016) Improving Neural Machine Translation Models with Monolingual Data. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, Berlin, 7-12 August 2016, 86-96. [Google Scholar] [CrossRef]
|
|
[11]
|
Caswell, I., Chelba, C. and Grangier, D. (2019) Tagged Back-Translation. Proceedings of the 4th Conference on Machine Translation, Florence, 1-2 August 2019, 53-63. [Google Scholar] [CrossRef]
|
|
[12]
|
Khatri, J. and Bhattacharyya, P. (2020) Filtering Back-Translated Data in Unsupervised Neural Machine Translation. Proceedings of the 28th International Conference on Computational Linguistics, Barcelona, 8-13 December 2020, 4334-4339. [Google Scholar] [CrossRef]
|
|
[13]
|
Wei, H., Zhang, Z., Chen, B. and Luo, W. (2020) Iterative Domain-Repaired Back-Translation. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, Online, 16-20 November 2020, 5884-5893. [Google Scholar] [CrossRef]
|
|
[14]
|
Abdulmumin, I., Galadanci, B.S. and Sinan, I.I. (2020) Iterative Self-Learning for Enhanced Back-Translation in Low Resource Neural Machine Translation. Proceedings of the 7th Workshop on Asian Translation, Suzhou, December 2020, 170-178.
|
|
[15]
|
Pham, H., Wang, X., Yang, Y. and Neubig, G. (2021) Meta Back-Translation. arXiv: 2102.07847.
|
|
[16]
|
Jiao, R., Yang, Z., Sun, M. and Liu, Y. (2021) Alternated Training with Synthetic and Authentic Data for Neural Machine Translation. Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, Online, 1-6 August 2021, 1828-1834. [Google Scholar] [CrossRef]
|
|
[17]
|
Zhang, J. and Matsumoto, T. (2019) Corpus Augmentation for Neural Machine Translation with Chinese-Japanese Parallel Corpora. Applied Sciences, 9, Article 2036. [Google Scholar] [CrossRef]
|
|
[18]
|
尤丛丛, 高盛祥, 余正涛, 等. 基于同义词数据增强的汉越神经机器翻译方法[J]. 计算机工程与科学, 2021, 43(8): 1497-1502.
|
|
[19]
|
贾承勋, 赖华, 余正涛, 等. 基于短语替换的汉越伪平行句对生成[J]. 中文信息学报, 2021, 35(8): 47-55.
|
|
[20]
|
Song, K., Zhang, Y., Yu, H., Luo, W., Wang, K. and Zhang, M. (2019) Code-Switching for Enhancing NMT with Pre-Specified Translation. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Minneapolis, 2-7 June 2019, 449-459.
|
|
[21]
|
朱俊国, 杨福岸, 余正涛, 等. 低频词表示增强的低资源神经机器翻译[J]. 中文信息学报, 2022, 36(6): 44-51.
|
|
[22]
|
张一鸣, 刘俊鹏, 宋鼎新, 等. 融合数据增强与多样化解码的神经机器翻译[J]. 厦门大学学报(自然科学版), 2021, 60(4): 670-674.
|
|
[23]
|
Zhao, S., Wang, H., Liu, T. and Li, S. (2009) Extracting Paraphrase Patterns from Bilingual Parallel Corpora. Natural Language Engineering, 15, 503-526. [Google Scholar] [CrossRef]
|
|
[24]
|
Baziotis, C., Haddow, B. and Birch, A. (2020) Language Model Prior for Low-Resource Neural Machine Translation. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, Online, 16-20 November 2020, 7622-7634. [Google Scholar] [CrossRef]
|
|
[25]
|
朱志国, 郭军军, 余正涛. 一种Mask交互融合预训练知识的低资源神经机器翻译方法[J/OL]. 小型微型计算机系统: 1-8.
http://kns.cnki.net/kcms/detail/21.1106.tp.20221123.1125.022.html
|
|
[26]
|
王可超, 郭军军, 张亚飞, 高盛祥, 余正涛. 基于回译和比例抽取孪生网络筛选的汉越平行语料扩充方法[J]. 计算机工程与科学, 2022, 44(10): 1861-1868.
|
|
[27]
|
Ji, Y., Shi, L., Su, Y.L., Ren, Q., Wu, N. and Wang, H. (2021) A Strategy for Referential Problem in Low-Resource Neural Machine Translation. In: Farkaš, I., Masulli, P., Otte, S. and Wermter, S., Eds., Artificial Neural Networks and Machine Learning—ICANN 2021, Springer, Cham, 321-332. [Google Scholar] [CrossRef]
|
|
[28]
|
Song, K., Zhang, Y., Zhang, M. and Luo, W. (2018) Improved English to Russian Translation by Neural Suffix Prediction. Thirty-Second AAAI Conference on Artificial Intelligence, Riverside, 2-7 February 2018, 410-417.
|
|
[29]
|
Xiao, Y.L., Liu, L.M., Huang, G.P., Qu, C., Huang, S.J., Shi, S.M. and Chen, J.J. (2022) BiTIIMT: A Bilingual Text-Infilling Method for Interactive Machine Translation. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, Dublin, 22-27 May 2022, 1958-1969. [Google Scholar] [CrossRef]
|
|
[30]
|
Zhang, T., Ye, W., Yang, B.S., Zhang, L., Ren, X.Z., Liu, D., Sun, J., Zhang, S.K., Zhang, H.B. and Zhao, W. (2022) Frequency-Aware Contrastive Learning for Neural Machine Translation. Proceedings of the AAAI Conference on Artificial Intelligence, 36, 11712-11720. [Google Scholar] [CrossRef]
|
|
[31]
|
Currey, A. and Heafield, K. (2019) Incorporating Source Syntax into Transformer-Based Neural Machine Translation. Proceedings of the 4th Conference on Machine Translation, Florence, 1-2 August 2019, 24-33. [Google Scholar] [CrossRef]
|
|
[32]
|
Li, Y., Yin, Y., Li, J. and Zhang, Y. (2022) Prompt-Driven Neural Machine Translation. Findings of the Association for Computational Linguistics: ACL 2022, Dublin, 22-27 May 2022, 2579-2590. [Google Scholar] [CrossRef]
|
|
[33]
|
Sennrich, R. and Haddow, B. (2016) Linguistic Input Features Improve Neural Machine Translation. Proceedings of the First Conference on Machine Translation, Berlin, 11-12 August 2016, 83-91. [Google Scholar] [CrossRef]
|
|
[34]
|
Chen, K., Wang, R. and Utiyama, M. (2017) Neural Machine Translation with Source Dependency Representation. Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, Copenhagen, 7-11 September 2017, 2836-3842. [Google Scholar] [CrossRef]
|
|
[35]
|
Qing-dao-er-ji, R., Su, Y.L. and Liu, W.W. (2020) Research on the LSTM Mongolian and Chinese Machine Translation Based on Morpheme Encoding. Neural Computing and Applications, 32, 41-49. [Google Scholar] [CrossRef]
|
|
[36]
|
Zhang, J. and Matsumoto, T. (2017) Improving Character Level Japanese-Chinese Neural Machine Translation with Radicals as an Additional Input Feature. Proceedings of the 2017 International Conference on Asian Language Processing, Singapore, 5-7 December 2017, 172-175. [Google Scholar] [CrossRef]
|
|
[37]
|
Eriguchi, A., Hashimoto, K. and Tsuruoka, Y. (2016) Tree-to-Sequence Attentional Neural Machine Translation. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, Berlin, 7-12 August 2016, 823-833. [Google Scholar] [CrossRef]
|
|
[38]
|
Chen, H., Huang, S., Chiang, D. and Chen, J. (2017) Improved Neural Machine Translation with a Syntax-Aware Encoder and Decoder. Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, Vancouver, 30 July-4 August 2017, 1936-1945. [Google Scholar] [CrossRef]
|
|
[39]
|
Gū, J., Shavarani, H. and Sarkar, A. (2018) Top-Down Tree Structured Decoding with Syntactic Connections for Neural Machine Translation and Parsing. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, 31 October-4 November 2018, 401-413. [Google Scholar] [CrossRef]
|
|
[40]
|
Pham, T., Macháček, D. and Bojar, O. (2019) Promoting the Knowledge of Source Syntax in Transformer NMT Is Not Needed. Computación y Sistemas, 3, 923-934. [Google Scholar] [CrossRef]
|
|
[41]
|
Chang, T. and Rafferty, A.N. (2020) Encodings of Source Syntax: Similarities in NMT Representations across Target Languages. Proceedings of the 5th Workshop on Representation Learning for NLP, Online, 9 July 2020, 7-16. [Google Scholar] [CrossRef]
|
|
[42]
|
Zhang, M., Li, Z., Fu, G. and Zhang, M. (2019) Syntax-Enhanced Neural Machine Translation with Syntax-Aware Word Representations. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Minneapolis, 2-7 June 2019, 1151-1161. [Google Scholar] [CrossRef]
|
|
[43]
|
Wang, Y., Lee, H. and Chen, Y. (2019) Tree Transformer: Integrating Tree Structures into Self-Attention. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), Hong Kong, 3-7 November 2019, 1061-1070. [Google Scholar] [CrossRef]
|
|
[44]
|
Moussallem, D., Arcan, M., Ngomo, A.N. and Buitelaar, P. (2019) Augmenting Neural Machine Translation with Knowledge Graphs.
https://arxiv.org/abs/1902.08816
|
|
[45]
|
潘一荣, 李晓, 杨雅婷, 等. 面向维汉机器翻译的层次化多特征融合模型[J]. 厦门大学学报(自然科学版), 2020, 59(2): 206-212.
|
|
[46]
|
Duan, X., Ji, B., Jia, H., Tan, M., Zhang, M., Chen, B., Luo, W. and Zhang, Y. (2020) Bilingual Dictionary Based Neural Machine Translation without Using Parallel Sentences. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, Online, 5-10 July 2020, 1570-1579. [Google Scholar] [CrossRef]
|
|
[47]
|
满志博, 毛存礼, 余正涛, 李训宇, 高盛祥, 朱俊国. 基于多语言联合训练的汉-英-缅神经机器翻译方法[J]. 清华大学学报(自然科学版), 2021, 61(9): 927-935.
|
|
[48]
|
Chen, K., Wang, R., Utiyama, M. and Sumita, E. (2022) Integrating Prior Translation Knowledge into Neural Machine Translation. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 30, 330-339. [Google Scholar] [CrossRef]
|
|
[49]
|
Lu, Z., Li, X., Liu, Y., Zhou, C., Cui, J., Wang, B., Zhang, M. and Su, J. (2022) Exploring Multi-Stage Information Interactions for Multi-Source Neural Machine Translation. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 30, 562-570. [Google Scholar] [CrossRef]
|
|
[50]
|
亢晓勉, 宗成庆. 基于篇章结构多任务学习的神经机器翻译[J]. 软件学报, 2022, 33(10): 3806-3818.
|
|
[51]
|
Leong, C.K., Liu, X., Wong, D.F. and Chao, L.S. (2021) Exploiting Translation Model for Parallel Corpus Mining. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 29, 2829-2839. [Google Scholar] [CrossRef]
|
|
[52]
|
Li, X., Li, G., Liu, L., Meng, M. and Shi, S. (2019) On the Word Alignment from Neural Machine Translation. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, Florence, 28 July-2 August 2019, 1293-1303. [Google Scholar] [CrossRef]
|
|
[53]
|
邹翔, 朱俊国, 高盛祥, 余正涛, 杨福岸. 融入语言差异化特征的汉越神经机器翻译译文质量估计[J]. 小型微型计算机系统, 2022, 43(7): 1413-1418.
|
|
[54]
|
Song, K., Wang, K., Yu, H., Zhang, Y., Huang, Z., Luo, W., Duan, X. and Zhang, M. (2020) Alignment-Enhanced Transformer for Constraining NMT with Pre-Specified Translations. Proceedings of the AAAI Conference on Artificial Intelligence, 34, 8886-8893. [Google Scholar] [CrossRef]
|
|
[55]
|
Wang, W., Chen, G., Wang, H., Han, Y. and Chen, Y. (2022) Multilingual Sentence Transformer as a Multilingual Word Aligner. Findings of the Association for Computational Linguistics: EMNLP 2022, Abu Dhabi, December 2022, 2952-2963.
|
|
[56]
|
Zhang, L. and Komachi, M. (2018) Neural Machine Translation of Logographic Languages Using Sub-character Level Information. Proceedings of the Third Conference on Machine Translation: Research Papers, Brussels, 31 October-1 Novermber 2018, 17-25. [Google Scholar] [CrossRef]
|
|
[57]
|
Zhang, J. and Matsumoto, T. (2019) Character Decomposition for Japanese-Chinese Character-Level Neural Machine Translation. Proceedings of the 2019 International Conference on Asian Language Processing (IALP), Shanghai, 15-17 November 2019, 35-40. [Google Scholar] [CrossRef]
|
|
[58]
|
Zhang, L. and Komachi, M. (2019) Chinese-Japanese Unsupervised Neural Machine Translation Using Sub-character Level Information. The 33rd Pacific Asia Conference on Language, Information and Computation (PACLIC 33), Hakodate, 13-15 September 2019, 309-315.
|
|
[59]
|
Du, J. and Way, A. (2017) Pinyin as Subword Unit for Chinese-Sourced Neural Machine Translation. Proceedings of the 2017 International Conference on Artificial Intelligence and Computer Science, Las Vegas, 14-16 December 2017, 89-101.
|
|
[60]
|
Tan, M., Hu, Y., Nikolov, N.I. and Hahnloser, R. (2018) Wubi2en: Character-Level Chinese-English Translation through ASCII Encoding. Proceedings of the Third Conference on Machine Translation: Research Papers, Brussels, 31 October-1 November 2018, 10-16.
|
|
[61]
|
Zhang, W., Lin, F., Wang, X., Liang, Z. and Huang, Z. (2019) SubCharacter Chinese-English Neural Machine Translation with Wubi Encoding.
https://arxiv.org/abs/1911.02737
|
|
[62]
|
Halpern, J. (2018) Very Large-Scale Lexical Resources to Enhance Chinese and Japanese Machine Translation. Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018), Miyazaki, 7-12 May 2018, 857-861.
|
|
[63]
|
Saunders, D., Feely, W. and Byrne, B. (2020) Inference-Only Sub-Character Decomposition Improves Translation of Unseen Logographic Characters. Proceedings of the 7th Workshop on Asian Translation, Suzhou, December 2020, 170-177.
|
|
[64]
|
中澤敏明, Chu, C., 黒橋禎夫. 日中共通漢字の整理とこれを利用した日中機械翻訳の高度化[EB/OL]. Japio Year Book, 258-261.
https://cir.nii.ac.jp/crid/1523669555917032960, 2023-05-30.
|
|
[65]
|
ト朝暉. 日中機械翻訳に関する研究～とりたて表現, 否定表現の翻訳規則を中心に～[D]: [博士学位论文]. 岐阜: 岐阜大学, 2004.
|
|
[66]
|
罗雯涛. 关于日中神经网络机器翻译中的词汇问题的探讨[J]. 计算机科学与应用, 2020(3): 387-397.
|