{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T21:50:38Z","timestamp":1780955438822,"version":"3.54.1"},"publisher-location":"Singapore","reference-count":34,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819947515","type":"print"},{"value":"9789819947522","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-981-99-4752-2_53","type":"book-chapter","created":{"date-parts":[[2023,7,30]],"date-time":"2023-07-30T16:02:10Z","timestamp":1690732930000},"page":"646-657","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A Data Augmentation Method Based on Sub-tree Exchange for Low-Resource Neural Machine Translation"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-2035-5892","authenticated-orcid":false,"given":"Chuncheng","family":"Chi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2878-6119","authenticated-orcid":false,"given":"Fuxue","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1834-8956","authenticated-orcid":false,"given":"Hong","family":"Yan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4456-6674","authenticated-orcid":false,"given":"Hui","family":"Guan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-0116-3671","authenticated-orcid":false,"given":"Zhongchao","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,7,31]]},"reference":[{"key":"53_CR1","unstructured":"Gehring, J., Auli, M., Grangier, D., Yarats, D., Dauphin, Y.N.: Convolutional sequence to sequence learning. In: International Conference on Machine Learning, pp. 1243\u20131252. PMLR (2017)"},{"key":"53_CR2","unstructured":"Wu, Y., et al.: Google\u2019s neural machine translation system: bridging the gap between human and machine translation. arXiv preprint arXiv:1609.08144 (2016)"},{"key":"53_CR3","unstructured":"Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014)"},{"key":"53_CR4","doi-asserted-by":"crossref","unstructured":"Gu, J., Wang, Y., Chen, Y., Cho, K., Li, V.O.: Meta-learning for low-resource neural machine translation. arXiv preprint arXiv:1808.08437 (2018)","DOI":"10.18653\/v1\/D18-1398"},{"key":"53_CR5","doi-asserted-by":"crossref","unstructured":"Ren, S., Chen, W., Liu, S., Li, M., Zhou, M., Ma, S.: Triangular architecture for rare language translation. arXiv preprint arXiv:1805.04813 (2018)","DOI":"10.18653\/v1\/P18-1006"},{"key":"53_CR6","doi-asserted-by":"crossref","unstructured":"Zoph, B., Yuret, D., May, J., Knight, K.: Transfer learning for low-resource neural machine translation. arXiv preprint arXiv:1604.02201 (2016)","DOI":"10.18653\/v1\/D16-1163"},{"key":"53_CR7","doi-asserted-by":"crossref","unstructured":"Wang, X., Pham, H., Dai, Z., Neubig, G.: Switchout: an efficient data augmentation algorithm for neural machine translation. arXiv preprint arXiv:1808.07512 (2018)","DOI":"10.18653\/v1\/D18-1100"},{"key":"53_CR8","doi-asserted-by":"crossref","unstructured":"Fadaee, M., Bisazza, A., Monz, C.: Data augmentation for low-resource neural machine translation. arXiv preprint arXiv:1705.00440 (2017)","DOI":"10.18653\/v1\/P17-2090"},{"key":"53_CR9","doi-asserted-by":"crossref","unstructured":"Zhang, J., Zong, C.: Exploiting source-side monolingual data in neural machine translation. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 1535\u20131545 (2016)","DOI":"10.18653\/v1\/D16-1160"},{"key":"53_CR10","doi-asserted-by":"crossref","unstructured":"Sennrich, R., Haddow, B., Birch, A.: Improving neural machine translation models with monolingual data. arXiv preprint arXiv:1511.06709 (2015)","DOI":"10.18653\/v1\/P16-1009"},{"key":"53_CR11","unstructured":"Zhang, X., Zhao, J., LeCun, Y.: Character-level convolutional networks for text classification. In: Advances in Neural Information Processing Systems, vol. 28 (2015)"},{"key":"53_CR12","doi-asserted-by":"crossref","unstructured":"Artetxe, M., Labaka, G., Agirre, E., Cho, K.: Unsupervised neural machine translation. arXiv preprint arXiv:1710.11041 (2017)","DOI":"10.18653\/v1\/D18-1399"},{"key":"53_CR13","first-page":"1","volume-title":"CONFERENCE 2016, LNCS","author":"F Author","year":"2016","unstructured":"Author, F., Author, S.: Title of a proceedings paper. In: Editor, F., Editor, S. (eds.) CONFERENCE 2016, LNCS, vol. 9999, pp. 1\u201313. Springer, Heidelberg (2016)"},{"key":"53_CR14","unstructured":"Lample, G., Conneau, A., Denoyer, L., Ranzato, M.: Unsupervised machine translation using monolingual corpora only. arXiv preprint arXiv:1711.00043 (2017)"},{"key":"53_CR15","doi-asserted-by":"crossref","unstructured":"Iyyer, M., Manjunatha, V., Boyd-Graber, J., Daum\u00e9 III, H.: Deep unordered composition rivals syntactic methods for text classification. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 1681\u20131691 (2015)","DOI":"10.3115\/v1\/P15-1162"},{"key":"53_CR16","unstructured":"Xie, Z., et al.: Data noising as smoothing in neural network language models. arXiv preprint arXiv:1703.02573 (2017)"},{"key":"53_CR17","doi-asserted-by":"crossref","unstructured":"Manning, C.D., Surdeanu, M., Bauer, J., Finkel, J.R., Bethard, S., McClosky, D.: The Stanford coreNLP natural language processing toolkit. In: Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp. 55\u201360 (2014)","DOI":"10.3115\/v1\/P14-5010"},{"key":"53_CR18","doi-asserted-by":"crossref","unstructured":"Burlot, F., Yvon, F.: Using monolingual data in neural machine translation: a systematic study. arXiv preprint arXiv:1903.11437 (2019)","DOI":"10.18653\/v1\/W18-6315"},{"key":"53_CR19","doi-asserted-by":"crossref","unstructured":"Cheng, Y., Cheng, Y.: Semi-supervised learning for neural machine translation. Jt. Train. Neural Mach. Transl. 25\u201340 (2019)","DOI":"10.1007\/978-981-32-9748-7_3"},{"key":"53_CR20","unstructured":"Cotterell, R., Kreutzer, J.: Explaining and generalizing back-translation through wake-sleep. arXiv preprint arXiv:1806.04402 (2018)"},{"key":"53_CR21","doi-asserted-by":"crossref","unstructured":"Currey, A., Miceli-Barone, A.V., Heafield, K.: Copied monolingual data improves low-resource neural machine translation. In: Proceedings of the Second Conference on Machine Translation, pp. 148\u2013156 (2017)","DOI":"10.18653\/v1\/W17-4715"},{"key":"53_CR22","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1007\/978-3-030-22747-0_7","volume-title":"Computational Science \u2013 ICCS 2019","author":"X Wu","year":"2019","unstructured":"Wu, X., Lv, S., Zang, L., Han, J., Hu, S.: Conditional BERT Contextual Augmentation. In: Rodrigues, J.M.F., et al. (eds.) ICCS 2019. LNCS, vol. 11539, pp. 84\u201395. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-22747-0_7"},{"key":"53_CR23","doi-asserted-by":"crossref","unstructured":"Kobayashi, S.: Contextual augmentation: data augmentation by words with paradigmatic relations. arXiv preprint arXiv:1805.06201 (2018)","DOI":"10.18653\/v1\/N18-2072"},{"key":"53_CR24","doi-asserted-by":"crossref","unstructured":"Gao, F., et al.: Soft contextual data augmentation for neural machine translation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 5539\u20135544 (2019)","DOI":"10.18653\/v1\/P19-1555"},{"key":"53_CR25","doi-asserted-by":"crossref","unstructured":"Chen, K., Wang, R., Utiyama, M., Sumita, E.: Content word aware neural machine translation. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 358\u2013364 (2020)","DOI":"10.18653\/v1\/2020.acl-main.34"},{"key":"53_CR26","doi-asserted-by":"crossref","unstructured":"Shi, X., Padhi, I., Knight, K.: Does string-based neural MT learn source syntax? In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 1526\u20131534 (2016)","DOI":"10.18653\/v1\/D16-1159"},{"key":"53_CR27","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"53_CR28","doi-asserted-by":"crossref","unstructured":"Ott, M., et al.: fairseq: A fast, extensible toolkit for sequence modeling. arXiv preprint arXiv:1904.01038 (2019)","DOI":"10.18653\/v1\/N19-4009"},{"key":"53_CR29","doi-asserted-by":"crossref","unstructured":"Lin, Z., Wu, L., Wang, M., Li, L.: Learning language specific sub-network for multilingual machine translation. arXiv preprint arXiv:2105.09259 (2021)","DOI":"10.18653\/v1\/2021.acl-long.25"},{"key":"53_CR30","doi-asserted-by":"crossref","unstructured":"Sennrich, R., Haddow, B., Birch, A.: Neural machine translation of rare words with subword units. arXiv preprint arXiv:1508.07909 (2015)","DOI":"10.18653\/v1\/P16-1162"},{"key":"53_CR31","doi-asserted-by":"crossref","unstructured":"Bugliarello, E., Okazaki, N.: Enhancing machine translation with dependency-aware self-attention. arXiv preprint arXiv:1909.03149 (2019)","DOI":"10.18653\/v1\/2020.acl-main.147"},{"key":"53_CR32","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"53_CR33","doi-asserted-by":"crossref","unstructured":"Papineni, K., Roukos, S., Ward, T., Zhu, W.J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311\u2013318 (2002)","DOI":"10.3115\/1073083.1073135"},{"issue":"1","key":"53_CR34","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1002\/int.22616","volume":"37","author":"M Maimaiti","year":"2022","unstructured":"Maimaiti, M., Liu, Y., Luan, H., Sun, M.: Data augmentation for low-resource languages NMT guided by constrained sampling. Int. J. Intell. Syst. 37(1), 30\u201351 (2022)","journal-title":"Int. J. Intell. Syst."}],"container-title":["Lecture Notes in Computer Science","Advanced Intelligent Computing Technology and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-4752-2_53","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,1]],"date-time":"2023-08-01T23:15:11Z","timestamp":1690931711000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-4752-2_53"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9789819947515","9789819947522"],"references-count":34,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-4752-2_53","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"31 July 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Zhengzhou","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 August 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 August 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icic2023a","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ic-icc.cn\/2023\/index.htm","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}