{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,22]],"date-time":"2026-02-22T07:01:02Z","timestamp":1771743662993,"version":"3.50.1"},"publisher-location":"Singapore","reference-count":23,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819570805","type":"print"},{"value":"9789819570812","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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":[[2026]]},"DOI":"10.1007\/978-981-95-7081-2_29","type":"book-chapter","created":{"date-parts":[[2026,2,22]],"date-time":"2026-02-22T06:45:53Z","timestamp":1771742753000},"page":"446-459","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["SASP-NMT: Syntax-Aware Structured Prompting for\u00a0Low-Resource Neural Machine Translation"],"prefix":"10.1007","author":[{"given":"Hao","family":"Xing","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nier","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yang","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yatu","family":"Ji","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuo","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Min","family":"Lu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,2,23]]},"reference":[{"key":"29_CR1","doi-asserted-by":"publisher","unstructured":"Hendy, A., et al.: How good are gpt models at machine translation? a comprehensive evaluation. arXiv e-prints arXiv:2302.09210 (2023). https:\/\/doi.org\/10.48550\/arXiv.2302.09210","DOI":"10.48550\/arXiv.2302.09210"},{"key":"29_CR2","doi-asserted-by":"publisher","unstructured":"Zhu, W., et al.: Multilingual machine translation with large language models: empirical results and analysis. In: Duh, K., Gomez, H., Bethard, S. (eds.) Findings of the Association for Computational Linguistics: NAACL 2024, pp. 2765\u20132781. Association for Computational Linguistics, Mexico City, Mexico (2024). https:\/\/doi.org\/10.18653\/v1\/2024.findings-naacl.176","DOI":"10.18653\/v1\/2024.findings-naacl.176"},{"key":"29_CR3","doi-asserted-by":"publisher","unstructured":"Hangya, V., Saadi, H.S., Fraser, A.: Improving low-resource languages in pre-trained multilingual language models. In: Goldberg, Y., Kozareva, Z., Zhang, Y. (eds.) Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pp. 11993\u201312006. Association for Computational Linguistics, Abu Dhabi, United Arab Emirates (2022). https:\/\/doi.org\/10.18653\/v1\/2022.emnlp-main.822","DOI":"10.18653\/v1\/2022.emnlp-main.822"},{"key":"29_CR4","doi-asserted-by":"publisher","unstructured":"Bang, Y., et al.: A multitask, multilingual, multimodal evaluation of ChatGPT on reasoning, hallucination, and interactivity. In: Park, J.C., Arase, Y., Hu, B., Lu, W., Wijaya, D., Purwarianti, A., Krisnadhi, A.A. (eds.) Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 675\u2013718. Association for Computational Linguistics, Nusa Dua, Bali (2023). https:\/\/doi.org\/10.18653\/v1\/2023.ijcnlp-main.45","DOI":"10.18653\/v1\/2023.ijcnlp-main.45"},{"key":"29_CR5","doi-asserted-by":"publisher","unstructured":"Gao, Y., Wang, R., Hou, F.: How to Design Translation Prompts for ChatGPT: an Empirical Study. arXiv e-prints arXiv:2304.02182 (2023). https:\/\/doi.org\/10.48550\/arXiv.2304.02182","DOI":"10.48550\/arXiv.2304.02182"},{"key":"29_CR6","doi-asserted-by":"crossref","unstructured":"Reheman, A., Zhou, T., Luo, Y., Yang, D., Xiao, T., Zhu, J.: Prompting neural machine translation with translation memories. Proc. AAAI Conf. Artif. Intell. 37(11), 13519\u201313527 (2023)","DOI":"10.1609\/aaai.v37i11.26585"},{"key":"29_CR7","doi-asserted-by":"publisher","unstructured":"Vilar, D., Freitag, M., Cherry, C., Luo, J., Ratnakar, V., Foster, G.: Prompting PaLM for translation: assessing strategies and performance. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). pp. 15406\u201315427. Association for Computational Linguistics, Toronto, Canada (2023). https:\/\/doi.org\/10.18653\/v1\/2023.acl-long.859","DOI":"10.18653\/v1\/2023.acl-long.859"},{"key":"29_CR8","doi-asserted-by":"publisher","unstructured":"Wang, J., Meng, F., Zhang, Y., Zhou, J.: Retrieval-augmented machine translation with unstructured knowledge. arXiv e-prints arXiv:2412.04342 (2024). https:\/\/doi.org\/10.48550\/arXiv.2412.04342","DOI":"10.48550\/arXiv.2412.04342"},{"key":"29_CR9","doi-asserted-by":"publisher","unstructured":"Chang, C.C., Li, C.F., Lee, C.H., Lee, H.S.: enhancing low-resource minority language translation with LLMs and retrieval-augmented generation for cultural nuances. arXiv e-prints arXiv:2505.10829 (2025). https:\/\/doi.org\/10.48550\/arXiv.2505.10829","DOI":"10.48550\/arXiv.2505.10829"},{"key":"29_CR10","doi-asserted-by":"publisher","unstructured":"Lin, X.V., et al.: Few-shot learning with multilingual generative language models. In: Goldberg, Y., Kozareva, Z., Zhang, Y. (eds.) Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pp. 9019\u20139052. Association for Computational Linguistics, Abu Dhabi, United Arab Emirates (2022). https:\/\/doi.org\/10.18653\/v1\/2022.emnlp-main.616","DOI":"10.18653\/v1\/2022.emnlp-main.616"},{"key":"29_CR11","doi-asserted-by":"publisher","unstructured":"Zhang, S., et al.: OPT: Open pre-trained transformer language models. arXiv e-prints arXiv:2205.01068 (2022). https:\/\/doi.org\/10.48550\/arXiv.2205.01068","DOI":"10.48550\/arXiv.2205.01068"},{"key":"29_CR12","unstructured":"Kocmi, T., Federmann, C.: Large language models are state-of-the-art evaluators of translation quality. In: Nurminen, M., et al., (eds.) Proceedings of the 24th Annual Conference of the European Association for Machine Translation. pp. 193\u2013203. European Association for Machine Translation, Tampere, Finland (2023). https:\/\/aclanthology.org\/2023.eamt-1.19\/"},{"key":"29_CR13","doi-asserted-by":"publisher","unstructured":"Raunak, V., Sharaf, A., Wang, Y., Awadalla, H., Menezes, A.: Leveraging GPT-4 for automatic translation post-editing. In: Bouamor, H., Pino, J., Bali, K. (eds.) Findings of the Association for Computational Linguistics: EMNLP 2023, pp. 12009\u201312024. Association for Computational Linguistics, Singapore (2023). https:\/\/doi.org\/10.18653\/v1\/2023.findings-emnlp.804","DOI":"10.18653\/v1\/2023.findings-emnlp.804"},{"key":"29_CR14","doi-asserted-by":"publisher","unstructured":"Wang, L., Lyu, C., Ji, T., Zhang, Z., Yu, D., Shi, S., Tu, Z.: Document-level machine translation with large language models. In: Bouamor, H., Pino, J., Bali, K. (eds.) Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. pp. 16646\u201316661. Association for Computational Linguistics, Singapore (2023). https:\/\/doi.org\/10.18653\/v1\/2023.emnlp-main.1036","DOI":"10.18653\/v1\/2023.emnlp-main.1036"},{"key":"29_CR15","unstructured":"Moslem, Y., Haque, R., Kelleher, J.D., Way, A.: Adaptive machine translation with large language models. In: Nurminen, M., et al., (eds.) Proceedings of the 24th Annual Conference of the European Association for Machine Translation, pp. 227\u2013237. European Association for Machine Translation, Tampere, Finland (2023). https:\/\/aclanthology.org\/2023.eamt-1.22\/"},{"key":"29_CR16","doi-asserted-by":"publisher","unstructured":"Puduppully, R., Kunchukuttan, A., Dabre, R., Aw, A.T., Chen, N.: DecoMT: Decomposed prompting for machine translation between related languages using large language models. In: Bouamor, H., Pino, J., Bali, K. (eds.) Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pp. 4586\u20134602. Association for Computational Linguistics, Singapore (2023). https:\/\/doi.org\/10.18653\/v1\/2023.emnlp-main.279","DOI":"10.18653\/v1\/2023.emnlp-main.279"},{"key":"29_CR17","doi-asserted-by":"publisher","unstructured":"Peng, K., et al.: Towards making the most of ChatGPT for machine translation. In: Bouamor, H., Pino, J., Bali, K. (eds.) Findings of the Association for Computational Linguistics: EMNLP 2023, pp. 5622\u20135633. Association for Computational Linguistics, Singapore (2023). https:\/\/doi.org\/10.18653\/v1\/2023.findings-emnlp.373","DOI":"10.18653\/v1\/2023.findings-emnlp.373"},{"key":"29_CR18","doi-asserted-by":"publisher","unstructured":"Mu, Y., et al.: Augmenting large language model translators via translation memories. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Findings of the Association for Computational Linguistics: ACL 2023, pp. 10287\u201310299. Association for Computational Linguistics, Toronto, Canada (2023). https:\/\/doi.org\/10.18653\/v1\/2023.findings-acl.653","DOI":"10.18653\/v1\/2023.findings-acl.653"},{"key":"29_CR19","doi-asserted-by":"publisher","unstructured":"Fernandes, P., et al.: The devil is in the errors: leveraging large language models for fine-grained machine translation evaluation. In: Koehn, P., Haddow, B., Kocmi, T., Monz, C. (eds.) Proceedings of the Eighth Conference on Machine Translation, pp. 1066\u20131083. Association for Computational Linguistics, Singapore (2023). https:\/\/doi.org\/10.18653\/v1\/2023.wmt-1.100","DOI":"10.18653\/v1\/2023.wmt-1.100"},{"key":"29_CR20","doi-asserted-by":"publisher","unstructured":"Huang, H., et al.: Not all languages are created equal in LLMs: improving multilingual capability by cross-lingual-thought prompting. In: Bouamor, H., Pino, J., Bali, K. (eds.) Findings of the Association for Computational Linguistics: EMNLP 2023, pp. 12365\u201312394. Association for Computational Linguistics, Singapore (2023). https:\/\/doi.org\/10.18653\/v1\/2023.findings-emnlp.826","DOI":"10.18653\/v1\/2023.findings-emnlp.826"},{"key":"29_CR21","doi-asserted-by":"publisher","unstructured":"Feng, F., Yang, Y., Cer, D., Arivazhagan, N., Wang, W.: Language-agnostic BERT sentence embedding. In: Muresan, S., Nakov, P., Villavicencio, A. (eds.) Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 878\u2013891. Association for Computational Linguistics, Dublin, Ireland (2022). https:\/\/doi.org\/10.18653\/v1\/2022.acl-long.62","DOI":"10.18653\/v1\/2022.acl-long.62"},{"key":"29_CR22","doi-asserted-by":"publisher","unstructured":"Rei, R., Stewart, C., Farinha, A.C., Lavie, A.: COMET: A neural framework for MT evaluation. In: Webber, B., Cohn, T., He, Y., Liu, Y. (eds.) Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 2685\u20132702. Association for Computational Linguistics, Online (2020). https:\/\/doi.org\/10.18653\/v1\/2020.emnlp-main.213","DOI":"10.18653\/v1\/2020.emnlp-main.213"},{"key":"29_CR23","doi-asserted-by":"publisher","unstructured":"Sellam, T., Das, D., Parikh, A.: BLEURT: Learning robust metrics for text generation. In: Jurafsky, D., Chai, J., Schluter, N., Tetreault, J. (eds.) Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 7881\u20137892. Association for Computational Linguistics, Online (2020). https:\/\/doi.org\/10.18653\/v1\/2020.acl-main.704","DOI":"10.18653\/v1\/2020.acl-main.704"}],"container-title":["Lecture Notes in Computer Science","PRICAI 2025: Trends in Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-7081-2_29","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,22]],"date-time":"2026-02-22T06:45:55Z","timestamp":1771742755000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-7081-2_29"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9789819570805","9789819570812"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-7081-2_29","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"23 February 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRICAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Pacific Rim International Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Wellington","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"New Zealand","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 November 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 November 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"pricai2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.pricai.org\/2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}