{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,15]],"date-time":"2025-11-15T10:33:26Z","timestamp":1763202806960,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":22,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819981311"},{"type":"electronic","value":"9789819981328"}],"license":[{"start":{"date-parts":[[2023,11,26]],"date-time":"2023-11-26T00:00:00Z","timestamp":1700956800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,11,26]],"date-time":"2023-11-26T00:00:00Z","timestamp":1700956800000},"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":[[2024]]},"DOI":"10.1007\/978-981-99-8132-8_25","type":"book-chapter","created":{"date-parts":[[2023,11,25]],"date-time":"2023-11-25T10:02:23Z","timestamp":1700906543000},"page":"327-340","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["An Approach to\u00a0Mongolian Neural Machine Translation Based on\u00a0RWKV Language Model and\u00a0Contrastive Learning"],"prefix":"10.1007","author":[{"given":"Xu","family":"Liu","sequence":"first","affiliation":[]},{"given":"Yila","family":"Su","sequence":"additional","affiliation":[]},{"given":"Wu","family":"Nier","sequence":"additional","affiliation":[]},{"given":"Yatu","family":"Ji","sequence":"additional","affiliation":[]},{"given":"Ren","family":"Qing Dao Er Ji","sequence":"additional","affiliation":[]},{"given":"Min","family":"Lu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,11,26]]},"reference":[{"key":"25_CR1","first-page":"191","volume":"11","author":"J Chen","year":"2023","unstructured":"Chen, J., Tam, D., Raffel, C., Bansal, M., Yang, D.: An empirical survey of data augmentation for limited data learning in nlp. Trans. Assoc. Comput. Ling. 11, 191\u2013211 (2023)","journal-title":"Trans. Assoc. Comput. Ling."},{"key":"25_CR2","doi-asserted-by":"crossref","unstructured":"Feng, S.Y., et al.: A survey of data augmentation approaches for nlp. arXiv preprint arXiv:2105.03075 (2021)","DOI":"10.18653\/v1\/2021.findings-acl.84"},{"issue":"3","key":"25_CR3","doi-asserted-by":"publisher","first-page":"673","DOI":"10.1162\/coli_a_00446","volume":"48","author":"B Haddow","year":"2022","unstructured":"Haddow, B., Bawden, R., Barone, A.V.M., Helcl, J., Birch, A.: Survey of low-resource machine translation. Comput. Linguist. 48(3), 673\u2013732 (2022)","journal-title":"Comput. Linguist."},{"key":"25_CR4","doi-asserted-by":"crossref","unstructured":"Hayashi, T., et al.: Back-translation-style data augmentation for end-to-end asr. In: 2018 IEEE Spoken Language Technology Workshop (SLT), pp. 426\u2013433. IEEE (2018)","DOI":"10.1109\/SLT.2018.8639619"},{"key":"25_CR5","unstructured":"Kitaev, N., Kaiser, \u0141., Levskaya, A.: Reformer: The efficient transformer. arXiv preprint arXiv:2001.04451 (2020)"},{"key":"25_CR6","doi-asserted-by":"crossref","unstructured":"Lalis, J.T., Maravillas, E.: Dynamic forecasting of electric load consumption using adaptive multilayer perceptron (amlp). In: 2014 International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM), pp. 1\u20137. IEEE (2014)","DOI":"10.1109\/HNICEM.2014.7016237"},{"key":"25_CR7","doi-asserted-by":"publisher","first-page":"193907","DOI":"10.1109\/ACCESS.2020.3031549","volume":"8","author":"PH Le-Khac","year":"2020","unstructured":"Le-Khac, P.H., Healy, G., Smeaton, A.F.: Contrastive representation learning: A framework and review. IEEE Access 8, 193907\u2013193934 (2020)","journal-title":"IEEE Access"},{"key":"25_CR8","unstructured":"Lee, S., Lee, D.B., Hwang, S.J.: Contrastive learning with adversarial perturbations for conditional text generation. arXiv preprint arXiv:2012.07280 (2020)"},{"key":"25_CR9","doi-asserted-by":"crossref","unstructured":"Liu, P., Wang, X., Xiang, C., Meng, W.: A survey of text data augmentation. In: 2020 International Conference on Computer Communication and Network Security (CCNS), pp. 191\u2013195. IEEE (2020)","DOI":"10.1109\/CCNS50731.2020.00049"},{"key":"25_CR10","first-page":"64","volume":"5","author":"LR Medsker","year":"2001","unstructured":"Medsker, L.R., Jain, L.: Recurrent neural networks. Design Appli. 5, 64\u201367 (2001)","journal-title":"Design Appli."},{"key":"25_CR11","doi-asserted-by":"crossref","unstructured":"Moosavi-Dezfooli, S.M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1765\u20131773 (2017)","DOI":"10.1109\/CVPR.2017.17"},{"key":"25_CR12","doi-asserted-by":"crossref","unstructured":"Morris, J.X., Lifland, E., Yoo, J.Y., Grigsby, J., Jin, D., Qi, Y.: Textattack: a framework for adversarial attacks, data augmentation, and adversarial training in nlp. arXiv preprint arXiv:2005.05909 (2020)","DOI":"10.18653\/v1\/2020.emnlp-demos.16"},{"key":"25_CR13","unstructured":"Peng, B., et al.: Rwkv: reinventing rnns for the transformer era. arXiv preprint arXiv:2305.13048 (2023)"},{"key":"25_CR14","first-page":"6198","volume":"34","author":"A Robey","year":"2021","unstructured":"Robey, A., Chamon, L., Pappas, G.J., Hassani, H., Ribeiro, A.: Adversarial robustness with semi-infinite constrained learning. Adv. Neural. Inf. Process. Syst. 34, 6198\u20136215 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"25_CR15","doi-asserted-by":"crossref","unstructured":"Sugiyama, A., Yoshinaga, N.: Data augmentation using back-translation for context-aware neural machine translation. In: Proceedings of the Fourth Workshop on Discourse in Machine Translation (DiscoMT 2019), pp. 35\u201344 (2019)","DOI":"10.18653\/v1\/D19-6504"},{"key":"25_CR16","unstructured":"Tay, Y., Bahri, D., Metzler, D., Juan, D.C., Zhao, Z., Zheng, C.: Synthesizer: Rethinking self-attention for transformer models. In: International Conference on Machine Learning, pp. 10183\u201310192. PMLR (2021)"},{"key":"25_CR17","first-page":"24261","volume":"34","author":"IO Tolstikhin","year":"2021","unstructured":"Tolstikhin, I.O., Houlsby, N., Kolesnikov, A., Beyer, L., Zhai, X., Unterthiner, T., Yung, J., Steiner, A., Keysers, D., Uszkoreit, J., et al.: Mlp-mixer: An all-mlp architecture for vision. Adv. Neural. Inf. Process. Syst. 34, 24261\u201324272 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"25_CR18","unstructured":"Vaswani, Aet al.: Attention is all you need. In: Advances in Neural Information Processing Systems 30 (2017)"},{"key":"25_CR19","unstructured":"Wang, L.: Rrwkv: capturing long-range dependencies in rwkv. arXiv preprint arXiv:2306.05176 (2023)"},{"key":"25_CR20","unstructured":"Wang, S., Li, B.Z., Khabsa, M., Fang, H., Ma, H.: Linformer: self-attention with linear complexity. arXiv preprint arXiv:2006.04768 (2020)"},{"key":"25_CR21","unstructured":"Zhai, S., et al.: An attention free transformer. arXiv preprint arXiv:2105.14103 (2021)"},{"key":"25_CR22","unstructured":"Zhang, W., et al.: Gmlp: Building scalable and flexible graph neural networks with feature-message passing. arXiv preprint arXiv:2104.09880 (2021)"}],"container-title":["Communications in Computer and Information Science","Neural Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-8132-8_25","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T15:51:14Z","timestamp":1710258674000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-8132-8_25"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,26]]},"ISBN":["9789819981311","9789819981328"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-8132-8_25","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2023,11,26]]},"assertion":[{"value":"26 November 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICONIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Neural Information Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Changsha","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":"20 November 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 November 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iconip2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/iconip2023.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1274","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"650","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"51% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4.14","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.46","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}