{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T13:49:15Z","timestamp":1778161755667,"version":"3.51.4"},"reference-count":58,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2025,1,21]],"date-time":"2025-01-21T00:00:00Z","timestamp":1737417600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,21]],"date-time":"2025-01-21T00:00:00Z","timestamp":1737417600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No.61877051"],"award-info":[{"award-number":["No.61877051"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2025,4]]},"DOI":"10.1007\/s10489-025-06251-5","type":"journal-article","created":{"date-parts":[[2025,1,21]],"date-time":"2025-01-21T09:53:59Z","timestamp":1737453239000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Contrastive pre-training and instruction tuning for cross-lingual aspect-based sentiment analysis"],"prefix":"10.1007","volume":"55","author":[{"given":"Wenwen","family":"Zhao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhisheng","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Song","family":"Yu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shiyu","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4818-8770","authenticated-orcid":false,"given":"Li","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,1,21]]},"reference":[{"key":"6251_CR1","doi-asserted-by":"publisher","unstructured":"Pontiki M, Galanis D, Pavlopoulos J, Papageorgiou H, Androutsopoulos I, Manandhar S (2014) SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27\u201335. https:\/\/doi.org\/10.3115\/v1\/s14-2004","DOI":"10.3115\/v1\/s14-2004"},{"issue":"11","key":"6251_CR2","doi-asserted-by":"publisher","first-page":"11019","DOI":"10.1109\/TKDE.2022.3230975","volume":"35","author":"W Zhang","year":"2022","unstructured":"Zhang W, Li X, Deng Y, Bing L, Lam W (2022) A survey on aspect-based sentiment analysis: Tasks, methods, and challenges. IEEE Trans Knowl Data Eng 35(11):11019\u201311038. https:\/\/doi.org\/10.1109\/TKDE.2022.3230975","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"12","key":"6251_CR3","doi-asserted-by":"publisher","first-page":"16138","DOI":"10.1007\/s10489-022-04307-4","volume":"53","author":"Z Zhao","year":"2023","unstructured":"Zhao Z, Tang M, Zhao F, Zhang Z, Chen X (2023) Incorporating semantics, syntax and knowledge for aspect based sentiment analysis. Appl Intell 53(12):16138\u201316150. https:\/\/doi.org\/10.1007\/s10489-022-04307-4","journal-title":"Appl Intell"},{"key":"6251_CR4","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1016\/j.neucom.2022.03.027","volume":"489","author":"L Cheng","year":"2022","unstructured":"Cheng L, Chen Y, Liao Y (2022) Aspect-based sentiment analysis with component focusing multi-head co-attention networks. Neurocomputing 489:9\u201317. https:\/\/doi.org\/10.1016\/j.neucom.2022.03.027","journal-title":"Neurocomputing"},{"issue":"3","key":"6251_CR5","doi-asserted-by":"publisher","first-page":"279","DOI":"10.1007\/s41019-022-00187-3","volume":"7","author":"Y Xu","year":"2022","unstructured":"Xu Y, Cao H, Du W, Wang W (2022) A survey of cross-lingual sentiment analysis: Methodologies, models and evaluations. Data Sci. Eng. 7(3):279\u2013299. https:\/\/doi.org\/10.1007\/s41019-022-00187-3","journal-title":"Data Sci. Eng."},{"key":"6251_CR6","doi-asserted-by":"crossref","unstructured":"Han X, Zhang Z, Ding N, Gu Y, Liu X (2021) Pre-trained models: Past, present and future. AI Open 2:225\u2013250. https:\/\/doi.org\/10.1016\/j.aiopen.2021.08.002","DOI":"10.1016\/j.aiopen.2021.08.002"},{"issue":"9","key":"6251_CR7","doi-asserted-by":"publisher","first-page":"10096","DOI":"10.1007\/s10489-022-04046-6","volume":"53","author":"P Kumar","year":"2023","unstructured":"Kumar P, Pathania K, Raman B (2023) Zero-shot learning based cross-lingual sentiment analysis for sanskrit text with insufficient labeled data. Appl Intell 53(9):10096\u201310113. https:\/\/doi.org\/10.1007\/s10489-022-04046-6","journal-title":"Appl Intell"},{"key":"6251_CR8","doi-asserted-by":"publisher","unstructured":"Jebbara S, Cimiano P (2019) Zero-shot cross-lingual opinion target extraction. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2019, Minneapolis, MN, USA, June 2-7, 2019, Volume 1 (Long and Short Papers), pp. 2486\u20132495. https:\/\/doi.org\/10.18653\/v1\/n19-1257","DOI":"10.18653\/v1\/n19-1257"},{"key":"6251_CR9","doi-asserted-by":"publisher","unstructured":"Lin N, Fu Y, Lin X, Zhou D, Yang A, Jiang S (2023) Cl-xabsa: Contrastive learning for cross-lingual aspect-based sentiment analysis. IEEE\/ACM Transactions on Audio, Speech, and Language Processing 31:2935\u20132946. https:\/\/doi.org\/10.1109\/TASLP.2023.3297964","DOI":"10.1109\/TASLP.2023.3297964"},{"key":"6251_CR10","doi-asserted-by":"publisher","unstructured":"Zhang W, He R, Peng H, Bing L, Lam W (2021) Cross-lingual aspect-based sentiment analysis with aspect term code-switching. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, EMNLP 2021, Virtual Event \/ Punta Cana, Dominican Republic, 7-11 November, 2021, pp. 9220\u20139230. https:\/\/doi.org\/10.18653\/v1\/2021.emnlp-main.727","DOI":"10.18653\/v1\/2021.emnlp-main.727"},{"key":"6251_CR11","doi-asserted-by":"publisher","unstructured":"Pires T, Schlinger E, Garrette D (2019) How multilingual is multilingual BERT? In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 4996\u20135001. https:\/\/doi.org\/10.18653\/v1\/p19-1493","DOI":"10.18653\/v1\/p19-1493"},{"key":"6251_CR12","doi-asserted-by":"publisher","unstructured":"Conneau A, Khandelwal K, Goyal N, Chaudhary V, Wenzek G, Guzm\u00e1n F, Grave E, Ott M, Zettlemoyer L, Stoyanov V (2020) Unsupervised cross-lingual representation learning at scale. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 8440\u20138451. https:\/\/doi.org\/10.18653\/v1\/2020.acl-main.747","DOI":"10.18653\/v1\/2020.acl-main.747"},{"key":"6251_CR13","doi-asserted-by":"publisher","unstructured":"Barri\u00e8re V, Balahur A (2020) Improving sentiment analysis over non-english tweets using multilingual transformers and automatic translation for data-augmentation. In: Proceedings of the 28th International Conference on Computational Linguistics, COLING 2020, Barcelona, Spain (Online), December 8-13, 2020, pp. 266\u2013271. https:\/\/doi.org\/10.18653\/v1\/2020.coling-main.23","DOI":"10.18653\/v1\/2020.coling-main.23"},{"key":"6251_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2023.102031","volume":"102","author":"Y Zeng","year":"2024","unstructured":"Zeng Y, Yan W, Mai S, Hu H (2024) Disentanglement translation network for multimodal sentiment analysis. Inf. Fusion 102:102031. https:\/\/doi.org\/10.1016\/j.inffus.2023.102031","journal-title":"Inf. Fusion"},{"key":"6251_CR15","unstructured":"Li X, Bing L, Zhang W, Li Z, Lam W (2020) Unsupervised cross-lingual adaptation for sequence tagging and beyond. arXiv:2010.12405"},{"key":"6251_CR16","unstructured":"Ma S, Yang J, Huang H, Chi Z, Dong L, Zhang D, Awadalla HH, Muzio A, Eriguchi A, Singhal S, Song X, Menezes A, Wei F (2020) XLM-T: scaling up multilingual machine translation with pretrained cross-lingual transformer encoders. arXiv:2012.15547"},{"key":"6251_CR17","doi-asserted-by":"publisher","unstructured":"Aditya B, Rohmatillah M, Tai L, Chien J (2024) Attention-guided adaptation for code-switching speech recognition. In: IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2024, Seoul, Republic of Korea, April 14-19, 2024, pp. 10256\u201310260. https:\/\/doi.org\/10.1109\/ICASSP48485.2024.10446258","DOI":"10.1109\/ICASSP48485.2024.10446258"},{"key":"6251_CR18","doi-asserted-by":"publisher","unstructured":"Zhu Z, Cheng X, Huang Z, Chen D, Zou Y (2023) Enhancing code-switching for cross-lingual SLU: A unified view of semantic and grammatical coherence. In: Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, EMNLP 2023, Singapore, December 6-10, 2023, pp. 7849\u20137856. https:\/\/doi.org\/10.18653\/v1\/2023.emnlp-main.486","DOI":"10.18653\/v1\/2023.emnlp-main.486"},{"key":"6251_CR19","doi-asserted-by":"publisher","unstructured":"Qin L, Chen Q, Xie T, Li Q, Lou J, Che W, Kan M (2022) Gl-clef: A global-local contrastive learning framework for cross-lingual spoken language understanding. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), ACL 2022, Dublin, Ireland, May 22-27, 2022, pp. 2677\u20132686. https:\/\/doi.org\/10.18653\/v1\/2022.acl-long.191","DOI":"10.18653\/v1\/2022.acl-long.191"},{"key":"6251_CR20","unstructured":"Brown T, Mann B, Ryder N, Subbiah M, Kaplan JD (2020) Language models are few-shot learners. In: Advances in Neural Information Processing Systems, vol. 33, pp. 1877\u20131901. https:\/\/proceedings.neurips.cc\/paper\/2020\/hash\/1457c0d6bfcb4967418bfb8ac1 42f64a-Abstract.html"},{"key":"6251_CR21","unstructured":"OpenAI: GPT-4 technical report (2024). arXiv:2303.08774"},{"key":"6251_CR22","doi-asserted-by":"crossref","unstructured":"Wang Q, Ding K, Liang B, Yang M, Xu R (2023) Reducing spurious correlations in aspect-based sentiment analysis with explanation from large language models. In: Findings of the Association for Computational Linguistics: EMNLP 2023, Singapore, December 6-10, 2023, pp. 2930\u20132941. https:\/\/aclanthology.org\/2023.findings-emnlp.193","DOI":"10.18653\/v1\/2023.findings-emnlp.193"},{"key":"6251_CR23","doi-asserted-by":"publisher","unstructured":"Zhang B, Yang H, Zhou T, Babar A, Liu X (2023) Enhancing financial sentiment analysis via retrieval augmented large language models. In: 4th ACM International Conference on AI in Finance, ICAIF 2023, Brooklyn, NY, USA, November 27-29, 2023, pp. 349\u2013356. https:\/\/doi.org\/10.1145\/3604237.3626866","DOI":"10.1145\/3604237.3626866"},{"key":"6251_CR24","doi-asserted-by":"publisher","unstructured":"Zhang W, Deng Y, Liu B, Pan SJ, Bing L (2024) Sentiment analysis in the era of large language models: A reality check. https:\/\/doi.org\/10.48550\/arXiv.2305.15005","DOI":"10.48550\/arXiv.2305.15005"},{"key":"6251_CR25","doi-asserted-by":"publisher","unstructured":"Han R, Peng T, Yang C, Wang B, Liu L, Wan X (2023) Is information extraction solved by chatgpt? an analysis of performance, evaluation criteria, robustness and errors. https:\/\/doi.org\/10.48550\/arXiv.2305.14450","DOI":"10.48550\/arXiv.2305.14450"},{"key":"6251_CR26","doi-asserted-by":"publisher","unstructured":"Lewis M, Liu Y, Goyal N, Ghazvininejad M, Mohamed A, Levy O, Stoyanov V, Zettlemoyer L (2020) BART: denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020, Online, July 5-10, 2020, pp. 7871\u20137880. https:\/\/doi.org\/10.18653\/v1\/2020.acl-main.703","DOI":"10.18653\/v1\/2020.acl-main.703"},{"key":"6251_CR27","doi-asserted-by":"publisher","unstructured":"Xue L, Constant N, Roberts A, Kale M, Al-Rfou R et al (2021) mt5: A massively multilingual pre-trained text-to-text transformer. In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2021, Online, June 6-11, 2021, pp. 483\u2013498 (2https:\/\/doi.org\/10.18653\/v1\/2021.naacl-main.41","DOI":"10.18653\/v1\/2021.naacl-main.41"},{"key":"6251_CR28","doi-asserted-by":"publisher","unstructured":"Zhang W, Deng Y, Li X, Yuan Y, Bing L, Lam W (2021) Aspect sentiment quad prediction as paraphrase generation. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, EMNLP 2021, Virtual Event \/ Punta Cana, Dominican Republic, 7-11 November, 2021, pp. 9209\u20139219. https:\/\/doi.org\/10.18653\/v1\/2021.emnlp-main.726","DOI":"10.18653\/v1\/2021.emnlp-main.726"},{"key":"6251_CR29","doi-asserted-by":"publisher","unstructured":"Asai A, Kudugunta S, Yu X, Blevins T, Gonen H et al (2024) BUFFET: benchmarking large language models for few-shot cross-lingual transfer. In: Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), NAACL 2024, Mexico City, Mexico, June 16-21, 2024, pp. 1771\u20131800. https:\/\/doi.org\/10.18653\/v1\/2024.naacl-long.100","DOI":"10.18653\/v1\/2024.naacl-long.100"},{"key":"6251_CR30","doi-asserted-by":"publisher","unstructured":"Zhang W, Li X, Deng Y, Bing L, Lam W (2021) Towards generative aspect-based sentiment analysis. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, ACL\/IJCNLP 2021, (Volume 2: Short Papers), Virtual Event, August 1-6, 2021, pp. 504\u2013510. https:\/\/doi.org\/10.18653\/v1\/2021.acl-short.64","DOI":"10.18653\/v1\/2021.acl-short.64"},{"key":"6251_CR31","doi-asserted-by":"publisher","unstructured":"Deng Y, Zhang W, Pan SJ, Bing L (2023) Bidirectional generative framework for cross-domain aspect-based sentiment analysis. In: Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), ACL 2023, Toronto, Canada, July 9-14, 2023, pp. 12272\u201312285. https:\/\/doi.org\/10.18653\/v1\/2023.acl-long.686","DOI":"10.18653\/v1\/2023.acl-long.686"},{"key":"6251_CR32","doi-asserted-by":"publisher","unstructured":"Yan H, Dai J, Ji T, Qiu X, Zhang Z (2021) A unified generative framework for aspect-based sentiment analysis. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, ACL\/IJCNLP 2021, (Volume 1: Long Papers), Virtual Event, August 1-6, 2021, pp. 2416\u20132429. https:\/\/doi.org\/10.18653\/v1\/2021.acl-long.188","DOI":"10.18653\/v1\/2021.acl-long.188"},{"key":"6251_CR33","doi-asserted-by":"publisher","unstructured":"Mao Y, Shen Y, Yang J, Zhu X, Cai L (2022) Seq2path: Generating sentiment tuples as paths of a tree. In: Findings of the Association for Computational Linguistics: ACL 2022, Dublin, Ireland, May 22-27, 2022, pp. 2215\u20132225. https:\/\/doi.org\/10.18653\/v1\/2022.findings-acl.174","DOI":"10.18653\/v1\/2022.findings-acl.174"},{"issue":"9","key":"6251_CR34","doi-asserted-by":"publisher","first-page":"10096","DOI":"10.1007\/s10489-022-04046-6","volume":"53","author":"P Kumar","year":"2023","unstructured":"Kumar P, Pathania K, Raman B (2023) Zero-shot learning based cross-lingual sentiment analysis for sanskrit text with insufficient labeled data. Appl Intell 53(9):10096\u201310113. https:\/\/doi.org\/10.1007\/s10489-022-04046-6","journal-title":"Appl Intell"},{"key":"6251_CR35","doi-asserted-by":"publisher","unstructured":"Zhao X, Wan H, Qi K (2024) QPEN: quantum projection and quantum entanglement enhanced network for cross-lingual aspect-based sentiment analysis. In: Thirty-Eighth AAAI Conference on Artificial Intelligence, pp. 19670\u201319678. https:\/\/doi.org\/10.1609\/aaai.v38i17.29940","DOI":"10.1609\/aaai.v38i17.29940"},{"key":"6251_CR36","doi-asserted-by":"publisher","unstructured":"Lin XV, Mihaylov T, Artetxe M, Wang T, Chen S et al (2022) Few-shot learning with multilingual generative language models. In: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022, Abu Dhabi, United Arab Emirates, December 7-11, 2022, pp. 9019\u20139052 . https:\/\/doi.org\/10.18653\/v1\/2022.emnlp-main.616","DOI":"10.18653\/v1\/2022.emnlp-main.616"},{"key":"6251_CR37","doi-asserted-by":"publisher","unstructured":"Anil R, Dai AM, Firat O, Johnson M, Lepikhin D et\u00a0al (2023) Palm 2 technical report. https:\/\/doi.org\/10.48550\/arXiv.2305.10403","DOI":"10.48550\/arXiv.2305.10403"},{"key":"6251_CR38","unstructured":"Chowdhery A, Narang S, Devlin J, Bosma M, Mishra G et al (2023) Palm: Scaling language modeling with pathways. J. Mach. Learn. Res 24:240\u20131240113. http:\/\/jmlr.org\/papers\/v24\/22-1144.html"},{"key":"6251_CR39","doi-asserted-by":"publisher","unstructured":"Hedderich MA, Adelani DI, Zhu D, Alabi JO, Markus U, Klakow D (2020) Transfer learning and distant supervision for multilingual transformer models: A study on african languages. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, EMNLP 2020, Online, November 16-20, 2020, pp. 2580\u20132591. Association for Computational Linguistics. https:\/\/doi.org\/10.18653\/v1\/2020.emnlp-main.204","DOI":"10.18653\/v1\/2020.emnlp-main.204"},{"key":"6251_CR40","unstructured":"Khosla P, Teterwak P, Wang C, Sarna A, Tian Y, Isola P, Maschinot A, Liu C, Krishnan D (2020) Supervised contrastive learning. In: Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, Virtual. https:\/\/proceedings.neurips.cc\/paper\/2020\/hash\/d89a66c7c80a29b1bdbab0f2 a1a94af8-Abstract.html"},{"key":"6251_CR41","doi-asserted-by":"publisher","unstructured":"Wang D, Ding N, Li P, Zheng H (2021) CLINE: contrastive learning with semantic negative examples for natural language understanding. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, ACL\/IJCNLP 2021, (Volume 1: Long Papers), Virtual Event, August 1-6, 2021, pp. 2332\u20132342. https:\/\/doi.org\/10.18653\/v1\/2021.acl-long.181","DOI":"10.18653\/v1\/2021.acl-long.181"},{"key":"6251_CR42","doi-asserted-by":"crossref","unstructured":"Mukherjee R, Kannen N, Pandey SK, Goyal P (2023) CONTRASTE: supervised contrastive pre-training with aspect-based prompts for aspect sentiment triplet extraction. In: Findings of the Association for Computational Linguistics: EMNLP 2023, Singapore, December 6-10, 2023, pp. 12065\u201312080. https:\/\/aclanthology.org\/2023.findings-emnlp.807","DOI":"10.18653\/v1\/2023.findings-emnlp.807"},{"key":"6251_CR43","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2023.110648","volume":"274","author":"P Li","year":"2023","unstructured":"Li P, Li P, Xiao X (2023) Aspect-pair supervised contrastive learning for aspect-based sentiment analysis. Knowl. Based Syst 274:110648. https:\/\/doi.org\/10.1016\/j.knosys.2023.110648","journal-title":"Knowl. Based Syst"},{"key":"6251_CR44","doi-asserted-by":"publisher","unstructured":"Zhou J, Zhou J, Zhao J, Wang S, Shan H, Tao G, Zhang Q, Huang X (2023) A soft contrastive learning-based prompt model for few-shot sentiment analysis. https:\/\/doi.org\/10.48550\/arXiv.2312.10479","DOI":"10.48550\/arXiv.2312.10479"},{"key":"6251_CR45","doi-asserted-by":"publisher","unstructured":"Zhu P, Zhang W, Wang Y, Hu Q (2022) Multi-granularity inter-class correlation based contrastive learning for open set recognition. Int. J. Softw. Informatics 12(2):157\u2013175. https:\/\/doi.org\/10.21655\/ijsi.1673-7288.00266","DOI":"10.21655\/ijsi.1673-7288.00266"},{"key":"6251_CR46","unstructured":"Sanh V, Webson A, Raffel C, Bach SH, Sutawika L et al (2022) Multitask prompted training enables zero-shot task generalization. In: The Tenth International Conference on Learning Representations, ICLR 2022, Virtual Event, April 25-29, 2022. https:\/\/openreview.net\/forum?id=9Vrb9D0WI4"},{"key":"6251_CR47","doi-asserted-by":"crossref","unstructured":"Zheng G, Wang J, Yu L, Zhang X (2024) Instruction tuning with retrieval-based examples ranking for aspect-based sentiment analysis. In: Findings of the Association for Computational Linguistics, ACL 2024, Bangkok, Thailand and Virtual Meeting, August 11-16, 2024, pp. 4777\u20134788. https:\/\/aclanthology.org\/2024.findings-acl.284","DOI":"10.18653\/v1\/2024.findings-acl.284"},{"key":"6251_CR48","unstructured":"Wei J, Bosma M, Zhao VY, Guu K, Yu AW et al (2022) Finetuned language models are zero-shot learners. https:\/\/openreview.net\/forum?id=gEZrGCozdqR"},{"key":"6251_CR49","doi-asserted-by":"publisher","unstructured":"Scaria K, Gupta H, Sawant SA, Mishra S, Baral C (2023) Instructabsa: Instruction learning for aspect based sentiment analysis. https:\/\/doi.org\/10.48550\/arXiv.2302.08624","DOI":"10.48550\/arXiv.2302.08624"},{"key":"6251_CR50","doi-asserted-by":"publisher","unstructured":"Li C, Wang S, Zhang J, Zong C (2024) Improving in-context learning of multilingual generative language models with cross-lingual alignment. https:\/\/doi.org\/10.48550\/arXiv.2311.08089","DOI":"10.48550\/arXiv.2311.08089"},{"key":"6251_CR51","doi-asserted-by":"crossref","unstructured":"Nayak NV, Nan Y, Trost A, Bach SH (2024) Learning to generate instruction tuning datasets for zero-shot task adaptation. In: Findings of the Association for Computational Linguistics, ACL 2024, Bangkok, Thailand and Virtual Meeting, August 11-16, 2024, pp. 12585\u201312611. https:\/\/aclanthology.org\/2024.findings-acl.748","DOI":"10.18653\/v1\/2024.findings-acl.748"},{"key":"6251_CR52","doi-asserted-by":"publisher","unstructured":"Huang K, Hsu I, Natarajan P, Chang K, Peng N (2022) Multilingual generative language models for zero-shot cross-lingual event argument extraction. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), ACL 2022, Dublin, Ireland, May 22-27, 2022, pp. 4633\u20134646. https:\/\/doi.org\/10.18653\/v1\/2022.acl-long.317","DOI":"10.18653\/v1\/2022.acl-long.317"},{"key":"6251_CR53","doi-asserted-by":"publisher","unstructured":"See A, Liu PJ, Manning CD (2017) Get to the point: Summarization with pointer-generator networks. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 1073\u20131083.https:\/\/doi.org\/10.18653\/v1\/P17-1099","DOI":"10.18653\/v1\/P17-1099"},{"key":"6251_CR54","doi-asserted-by":"publisher","unstructured":"Pontiki M, Galanis D, Papageorgiou H, Androutsopoulos I, Manandhar S, AL-Smadi M, Al-Ayyoub M, Zhao Y, Qin B, De\u00a0Clercq O, Hoste V, Apidianaki M, Tannier X, Loukachevitch N, Kotelnikov E, Bel N, Jim\u00e9nez-Zafra SM, Eryi\u011fit G (2016) SemEval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016), pp. 19\u201330. https:\/\/doi.org\/10.18653\/v1\/s16-1002","DOI":"10.18653\/v1\/s16-1002"},{"key":"6251_CR55","doi-asserted-by":"publisher","unstructured":"Keung P, Lu Y, Szarvas G, Smith NA (2022) The multilingual amazon reviews corpus. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, EMNLP 2020, Online, November 16-20, 2020, pp. 4563\u20134568. https:\/\/doi.org\/10.18653\/v1\/2020.emnlp-main.369","DOI":"10.18653\/v1\/2020.emnlp-main.369"},{"key":"6251_CR56","unstructured":"Touvron H, Martin L, Stone K, Albert P, Almahairi A, Babaei Y et\u00a0al (2023) Llama 2: Open foundation and fine-tuned chat models. arXiv:2307.09288"},{"key":"6251_CR57","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2024.125059","volume":"257","author":"N Lin","year":"2024","unstructured":"Lin N, Zeng M, Liao X, Liu W, Yang A, Zhou D (2024) Addressing class-imbalance challenges in cross-lingual aspect-based sentiment analysis: Dynamic weighted loss and anti-decoupling. Expert Syst Appl 257:125059. https:\/\/doi.org\/10.1016\/j.eswa.2024.125059","journal-title":"Expert Syst Appl"},{"key":"6251_CR58","doi-asserted-by":"publisher","unstructured":"Huang L, Ma S, Zhang D, Wei F, Wang H (2022) Zero-shot cross-lingual transfer of prompt-based tuning with a unified multilingual prompt. In: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022, pp. 11488\u201311497. https:\/\/doi.org\/10.18653\/v1\/2022.emnlp-main.790","DOI":"10.18653\/v1\/2022.emnlp-main.790"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-025-06251-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-025-06251-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-025-06251-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,22]],"date-time":"2025-02-22T17:23:07Z","timestamp":1740244987000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-025-06251-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,21]]},"references-count":58,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2025,4]]}},"alternative-id":["6251"],"URL":"https:\/\/doi.org\/10.1007\/s10489-025-06251-5","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,21]]},"assertion":[{"value":"30 December 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 January 2025","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}},{"value":"The authors of the submitted manuscript declare that it does not involve any ethical issues.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical and informed consent for data used"}}],"article-number":"358"}}