{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,4,12]],"date-time":"2024-04-12T18:33:36Z","timestamp":1712946816897},"reference-count":23,"publisher":"Institute of Electronics, Information and Communications Engineers (IEICE)","issue":"5","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEICE Trans. Fundamentals"],"published-print":{"date-parts":[[2022,5,1]]},"DOI":"10.1587\/transfun.2021eap1121","type":"journal-article","created":{"date-parts":[[2021,11,18]],"date-time":"2021-11-18T22:12:12Z","timestamp":1637273532000},"page":"871-876","source":"Crossref","is-referenced-by-count":1,"title":["Research on Mongolian-Chinese Translation Model Based on Transformer with Soft Context Data Augmentation Technique"],"prefix":"10.1587","volume":"E105.A","author":[{"given":"Qing-dao-er-ji","family":"REN","sequence":"first","affiliation":[{"name":"School of Information Engineering, Inner Mongolia University of Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuan","family":"LI","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Inner Mongolia University of Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shi","family":"BAO","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Inner Mongolia University of Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yong-chao","family":"LIU","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Inner Mongolia University of Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiu-hong","family":"CHEN","sequence":"additional","affiliation":[{"name":"Inner Mongolia Autonomous Reg Peoples Hosp"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"532","reference":[{"key":"1","unstructured":"[1] N. Kalchbrenner and P. Blunsom, \u201cRecurrent continuous translation models,\u201d Proc. 2013 Conference on Empirical Methods in Natural Language Processing, pp.1700-1709, 2013."},{"key":"2","unstructured":"[2] I. Sutskever, O. Vinyals, and Q.V. Le, \u201cSequence to sequence learning with neural networks,\u201d arXiv preprint arXiv:1409.3215, 2014."},{"key":"3","unstructured":"[3] Y. Wu, M. Schuster, Z. Chen, Q.V. Le, \u201cGoogle's neural machine translation system: Bridging the gap between human and machine translation,\u201d 2016."},{"key":"4","unstructured":"[4] J. Gehring, M. Auli, D. Grangier, D. Yarats, and Y.N. Dauphin, \u201cConvolutional sequence to sequence learning,\u201d Proc. 34th International Conference on Machine Learning, ICML 2017, vol.70, pp.1243-1252, 2017."},{"key":"5","unstructured":"[5] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, \u0141. Kaiser, and I. Polosukhin, \u201cAttention is all you need,\u201d arXiv preprint arXiv:1706.03762, 2017."},{"key":"6","unstructured":"[6] H. Hassan, A. Aue, C. Chen, V. Chowdhary, J. Clark, C. Federmann, X. Huang, M. Junczys-Dowmunt, W. Lewis, M. Li, S. Liu, T.-Y. Liu, R. Luo, A. Menezes, T. Qin, F. Seide, X. Tan, F. Tian, L. Wu, S. Wu, Y. Xia, D. Zhang, Z. Zhang, and M. Zhou, \u201cAchieving human parity on automatic Chinese to english news translation,\u201d 2018."},{"key":"7","unstructured":"[7] M. Dehghani, S. Gouws, O. Vinyals, J. Uszkoreit, and \u0141. Kaiser, \u201cUniversal Transformers,\u201d 2018."},{"key":"8","unstructured":"[8] F. Wu, A. Fan, Bae. A. Baevski, Y.N. Dauphin, and M. Auli, \u201cPay less attention with lightweight and dynamic convolutions,\u201d 2019."},{"key":"9","doi-asserted-by":"publisher","unstructured":"[9] F. Meng and J. Zhang, \u201cDTMT: A novel deep transition architecture for neural machine translation,\u201d Proc. AAAI Conference on Artificial Intelligence, vol.33, pp.224-231, 2019. 10.1609\/aaai.v33i01.3301224","DOI":"10.1609\/aaai.v33i01.3301224"},{"key":"10","doi-asserted-by":"crossref","unstructured":"[10] R. Sennrich, B. Haddow, and A. Birch, \u201cImproving neural machine translation models with monolingual data,\u201d Proc. 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2016.","DOI":"10.18653\/v1\/P16-1009"},{"key":"11","doi-asserted-by":"crossref","unstructured":"[11] S. Edunov, M. Ott, M. Auli, and D. Grangier, \u201cUnderstanding back-translation at scale,\u201d 2018.","DOI":"10.18653\/v1\/D18-1045"},{"key":"12","doi-asserted-by":"crossref","unstructured":"[12] M. Junczys-Dowmunt \u201cMicrosoft's submission to the WMT2018 news translation task: How I learned to stop worrying and love the data,\u201d Proc. Third Conference on Machine Translation: Shared Task Papers, pp.425-430, Belgium, Brussels. Association for Computational Linguistics, 2018. 10.18653\/v1\/W18-6415","DOI":"10.18653\/v1\/W18-6415"},{"key":"13","doi-asserted-by":"crossref","unstructured":"[13] M. Junczys-Dowmunt, \u201cDual conditional cross-entropy filtering of noisy parallel corpora,\u201d Proc. Third Conference on Machine Translation: Shared Task Papers, 2018.","DOI":"10.18653\/v1\/W18-6478"},{"key":"14","unstructured":"[14] K. Song, X. Tan, T. Qin, J. Lu, and T.-Y, Liu, \u201cMASS: Masked sequence to sequence pre-training for language generation,\u201d 2019."},{"key":"15","doi-asserted-by":"crossref","unstructured":"[15] Y. Chen, H. Zhang, L. Liu, J. Tao, Q. Zhang, K. Yang, R. Xia and J. Xie, \u201cResearch on image inpainting algorithm of improved total variation minimization method,\u201d J. Ambient Intelligence and Humanized Computing, 2021.","DOI":"10.1007\/s12652-020-02778-2"},{"key":"16","doi-asserted-by":"crossref","unstructured":"[16] Y. Chen, L. Liu, V. Phonevilay, K. Gu, R. Xia, J. Xie, Q. Zhang, and K. Yang, \u201cImage super-resolution reconstruction based on feature map attention mechanism,\u201d Applied Intelligence, vol.51, pp.4367-4380, 2021.","DOI":"10.1007\/s10489-020-02116-1"},{"key":"17","unstructured":"[17] BaDaMa oder. Research on Chinese Mongolian Phrase Conversion Rules for Machine Translation, Inner Mongolia Education Press, 2005."},{"key":"18","unstructured":"[18] SRI Wang and and L. Gu, \u201cResearch on Chinese Mongolian machine translation and related technologies based on hybrid strategy,\u201d Inner Mongolia University, 2009."},{"key":"19","unstructured":"[19] C. Su, \u201cResearch on Mongolian Chinese statistical machine translation based on hierarchical phrase model,\u201d Inner Mongolia University, 2014."},{"key":"20","unstructured":"[20] J. Du, \u201cMongolian Chinese neural network machine translation technology integrating statistical machine translation features,\u201d Inner Mongolia University, 2017."},{"key":"21","unstructured":"[21] W. Fan, H. Hou, H. Wang, J. Wu, and J. Li, \u201cMongolian Chinese neural network machine translation model integrating prior information,\u201d Journal of Chinese Information Technology, vol.32, no.6, pp.36-43, 2018."},{"key":"22","unstructured":"[22] T. Bai, \u201cResearch on Mongolian Chinese neural network machine translation based on reinforcement learning,\u201d Inner Mongolia University, 2020."},{"key":"23","doi-asserted-by":"crossref","unstructured":"[23] F. Gao, J. Zhu, L. Wu, Y. Xia, T. Qin, X. Cheng, W. Zhou, and T.-Y, Liu, \u201cSoft contextual data augmentation for neural machine translation,\u201d Proc. 57th Annual Meeting of the Association for Computational Linguistics, pp.5539-5544, 2019. 10.18653\/v1\/P19-1555","DOI":"10.18653\/v1\/P19-1555"}],"container-title":["IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transfun\/E105.A\/5\/E105.A_2021EAP1121\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,7]],"date-time":"2022-05-07T03:27:07Z","timestamp":1651894027000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transfun\/E105.A\/5\/E105.A_2021EAP1121\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,1]]},"references-count":23,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2022]]}},"URL":"https:\/\/doi.org\/10.1587\/transfun.2021eap1121","relation":{},"ISSN":["0916-8508","1745-1337"],"issn-type":[{"value":"0916-8508","type":"print"},{"value":"1745-1337","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,1]]},"article-number":"2021EAP1121"}}