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Meta-ED: Cross-lingual Event Detection Using Meta-learning for Indian Languages

Authors:

Sudeshna Sarkar,

Pawan GoyalAuthors Info & Claims

ACM Transactions on Asian and Low-Resource Language Information Processing, Volume 22, Issue 2

Article No.: 46, Pages 1 - 22

https://doi.org/10.1145/3555340

Published: 21 February 2023 Publication History

Abstract

Lack of annotated data is a major concern in Event Detection (ED) tasks for low-resource languages. Cross-lingual ED seeks to address this issue by transferring information across various languages to improve overall performance. In this article, we propose a method for cross-lingual ED with a few training instances. We present a model agnostic meta-learning approach for few-shot cross-lingual ED that is able to find good parameter initialization and enables fast adaptation to new low-resource languages. We evaluate our model on four Indian languages. The results show that our approach significantly outperforms the base model.

References

[1]

Zishan Ahmad, Sovan Kumar Sahoo, Asif Ekbal, and Pushpak Bhattacharyya. 2018. A deep learning model for event extraction and classification in Hindi for disaster domain. 15th International Conference on Natural Language Processing, 127.

[2]

David Ahn. 2006. The stages of event extraction. In Proceedings of the Workshop on Annotating and Reasoning about Time and Events (Sydney, Australia) (ARTE’06). Association for Computational Linguistics, USA, 1–8.

Digital Library

[3]

Akari Asai, Jungo Kasai, Jonathan Clark, Kenton Lee, Eunsol Choi, and Hannaneh Hajishirzi. 2021. XOR QA: Cross-lingual open-retrieval question answering. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics, Online, 547–564. DOI:

[4]

M. Saiful Bari, Batool Haider, and Saab Mansour. 2021. Nearest neighbour few-shot learning for cross-lingual classification. arXiv preprint arXiv:2109.02221 (2021).

[5]

Jiaxin Chen, Li-Ming Zhan, Xiao-Ming Wu, and Fu-lai Chung. 2020. Variational metric scaling for metric-based meta-learning. In Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 34, AAAI Press, New York, NY, 3478–3485.

[6]

Yubo Chen, Liheng Xu, Kang Liu, Daojian Zeng, and Jun Zhao. 2015. Event extraction via dynamic multi-pooling convolutional neural networks. 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). Association for Computational Linguistics, Beijing, China, 167–176. DOI:

[7]

Zheng Chen and Heng Ji. 2009. Can one language bootstrap the other: A case study on event extraction. In Proceedings of the NAACL HLT 2009 Workshop on Semi-Supervised Learning for Natural Language Processing (Boulder, Colorado) (SemiSupLearn’09). Association for Computational Linguistics, USA, 66–74.

[8]

Zewen Chi, Li Dong, Furu Wei, Wenhui Wang, Xian-Ling Mao, and Heyan Huang. 2019. Cross-lingual natural language generation via pre-training. arXiv:1909.10481 [cs.CL]

[9]

Xin Cong, Shiyao Cui, Bowen Yu, Tingwen Liu, Yubin Wang, and Bin Wang. 2021. Few-shot event detection with prototypical amortized conditional random field. arXiv:2012.02353 [cs.CL]

[10]

Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer, and Veselin Stoyanov. 2019. Unsupervised cross-lingual representation learning at scale. arXiv preprint arXiv:1911.02116.

[11]

Shiyao Cui, Bowen Yu, Tingwen Liu, Zhenyu Zhang, Xuebin Wang, and Jinqiao Shi. 2020. Edge-enhanced graph convolution networks for event detection with syntactic relation. arXiv:2002.10757 [cs.CL]

[12]

Shumin Deng, Ningyu Zhang, Jiaojian Kang, Yichi Zhang, Wei Zhang, and Huajun Chen. 2020. Meta-learning with dynamic-memory-based prototypical network for few-shot event detection. Proceedings of the 13th International Conference on Web Search and Data Mining (Jan2020). DOI:

Digital Library

[13]

Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 [cs.CL]

[14]

Zi-Yi Dou, Keyi Yu, and Antonios Anastasopoulos. 2019. Investigating meta-learning algorithms for low-resource natural language understanding tasks. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, 1192–1197. DOI:

[15]

Rotem Dror, Gili Baumer, Segev Shlomov, and Roi Reichart. 2018. The hitchhiker’s guide to testing statistical significance in natural language processing. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 1383–1392.

[16]

Seth Ebner, Patrick Xia, Ryan Culkin, Kyle Rawlins, and Benjamin Van Durme. 2020. Multi-sentence argument linking. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, Online, 8057–8077. DOI:

[17]

Chelsea Finn, Pieter Abbeel, and Sergey Levine. 2017. Model-agnostic meta-learning for fast adaptation of deep networks. In International Conference on Machine Learning. PMLR, 1126–1135.

Digital Library

[18]

Robert French. 1999. Catastrophic forgetting in connectionist networks. Trends in Cognitive Sciences 3 (51999), 128–135. DOI:

[19]

Alexander Fritzler, Varvara Logacheva, and Maksim Kretov. 2019. Few-shot classification in named entity recognition task. In Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing. 993–1000.

Digital Library

[20]

Victor Garcia and Joan Bruna. 2017. Few-shot learning with graph neural networks. arXiv preprint arXiv:1711.04043 (2017).

[21]

Jiatao Gu, Yong Wang, Yun Chen, Kyunghyun Cho, and Victor O. K. Li. 2018. Meta-learning for low-resource neural machine translation. arXiv:1808.08437 [cs.CL]

[22]

Yu Hong, Jianfeng Zhang, Bin Ma, Jianmin Yao, Guodong Zhou, and Qiaoming Zhu. 2011. Using cross-entity inference to improve event extraction. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics, Portland, OR, 1127–1136. https://aclanthology.org/P11-1113.

Digital Library

[23]

Yutai Hou, Wanxiang Che, Yongkui Lai, Zhihan Zhou, Yijia Liu, Han Liu, and Ting Liu. 2020. Few-shot slot tagging with collapsed dependency transfer and label-enhanced task-adaptive projection network. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, Online, 1381–1393. DOI:

[24]

Andrew Hsi, Yiming Yang, Jaime Carbonell, and Ruochen Xu. 2016. Leveraging multilingual training for limited resource event extraction. In Proceedings of the 26th International Conference on Computational Linguistics (COLING’16): Technical Papers. The COLING 2016 Organizing Committee, Osaka, Japan, 1201–1210. https://aclanthology.org/C16-1114.

[25]

Heng Ji and Ralph Grishman. 2008. Refining event extraction through cross-document inference. In Proceedings of ACL-08: HLT. Association for Computational Linguistics, Columbus, OH, 254–262. https://aclanthology.org/P08-1030.

[26]

Divyanshu Kakwani, Anoop Kunchukuttan, Satish Golla, N. C. Gokul, Avik Bhattacharyya, Mitesh M. Khapra, and Pratyush Kumar. 2020. iNLPSuite: Monolingual corpora, evaluation benchmarks and pre-trained multilingual language models for Indian languages. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings. 4948–4961.

[27]

Yunsu Kim, Yingbo Gao, and Hermann Ney. 2019. Effective cross-lingual transfer of neural machine translation models without shared vocabularies. arXiv preprint arXiv:1905.05475

[28]

D. P. Kingma and J. Ba. 2015. Adam: A method for stochastic optimization. In ICLR.

[29]

Vishwajeet Kumar, Nitish Joshi, Arijit Mukherjee, Ganesh Ramakrishnan, and Preethi Jyothi. 2019. Cross-lingual training for automatic question generation. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, Florence, Italy, 4863–4872. DOI:

[30]

Viet Dac Lai, Franck Dernoncourt, and Thien Huu Nguyen. 2020. Exploiting the matching information in the support set for few shot event classification. Advances in Knowledge Discovery and Data Mining: 24th Pacific-Asia Conference, Springer-Verlag, Berlin, Heidelberg, 233–245.

Digital Library

[31]

Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, and Radu Soricut. 2019. ALBERT: A lite BERT for self-supervised learning of language representations. arXiv preprint arXiv:1909.11942

[32]

Anne Lauscher, Vinit Ravishankar, Ivan Vulić, and Goran Glavaš. 2020. From zero to hero: On the limitations of zero-shot cross-lingual transfer with multilingual transformers. arXiv preprint arXiv:2005.00633

[33]

Jens Lehmann, Robert Isele, Max Jakob, Anja Jentzsch, Dimitris Kontokostas, Pablo N. Mendes, Sebastian Hellmann, Mohamed Morsey, Patrick Van Kleef, Sören Auer, et al. 2015. Dbpedia–a large-scale, multilingual knowledge base extracted from Wikipedia. Semantic Web 6, 2 (2015), 167–195.

[34]

Qi Li, Heng Ji, and Liang Huang. 2013. Joint event extraction via structured prediction with global features. In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, Sofia, Bulgaria, 73–82. https://aclanthology.org/P13-1008.

[35]

Shasha Liao and Ralph Grishman. 2010. Using document level cross-event inference to improve event extraction. In Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, Uppsala, Sweden, 789–797. https://aclanthology.org/P10-1081.

Digital Library

[36]

Jian Liu, Yubo Chen, Kang Liu, and Jun Zhao. 2019. Neural cross-lingual event detection with minimal parallel resources. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, 738–748. DOI:

[37]

Shaobo Liu, Rui Cheng, Xiaoming Yu, and Xueqi Cheng. 2018. Exploiting contextual information via dynamic memory network for event detection. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, Brussels, Belgium, 1030–1035. DOI:

[38]

Dongfang Lou, Zhilin Liao, Shumin Deng, Ningyu Zhang, and Huajun Chen. 2021. MLBiNet: A cross-sentence collective event detection network. arXiv:2105.09458 [cs.CL]

[39]

Yaojie Lu, Hongyu Lin, Jin Xu, Xianpei Han, Jialong Tang, Annan Li, Le Sun, Meng Liao, and Shaoyi Chen. 2021. Text2Event: Controllable sequence-to-structure generation for end-to-end event extraction. arXiv:2106.09232 [cs.CL]

[40]

Ayush Maheshwari, Hrishikesh Patel, Nandan Rathod, Ritesh Kumar, Ganesh Ramakrishnan, and Pushpak Bhattacharyya. 2019. Tale of tails using rule augmented sequence labeling for event extraction. DOI:

[41]

Hieu Man Duc Trong, Duc Trong Le, Amir Pouran Ben Veyseh, Thuat Nguyen, and Thien Huu Nguyen. 2020. Introducing a new dataset for event detection in cybersecurity texts. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP’20). Association for Computational Linguistics, Online, 5381–5390. DOI:

[42]

Akshay Mehra and Jihun Hamm. 2019. Penalty method for inversion-free deep bilevel optimization. arXiv preprint arXiv:1911.03432

[43]

Tsendsuren Munkhdalai and Hong Yu. 2017. Meta networks. In International Conference on Machine Learning. PMLR, 2554–2563.

Digital Library

[44]

Roberto Navigli and Simone Paolo Ponzetto. 2012. BabelNet: The automatic construction, evaluation and application of a wide-coverage multilingual semantic network. Artificial Intelligence 193 (2012), 217–250.

Digital Library

[45]

Thien Huu Nguyen, Kyunghyun Cho, and Ralph Grishman. 2016. Joint event extraction via recurrent neural networks. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics, San Diego, CA, 300–309. DOI:

[46]

Thien Huu Nguyen and Ralph Grishman. 2015. Event detection and domain adaptation with convolutional neural networks. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers). Association for Computational Linguistics, Beijing, China, 365–371. DOI:

[47]

Alex Nichol, Joshua Achiam, and John Schulman. 2018. On first-order meta-learning algorithms. arXiv preprint arXiv:1803.02999

[48]

Farhad Nooralahzadeh, Giannis Bekoulis, Johannes Bjerva, and Isabelle Augenstein. 2020. Zero-shot cross-lingual transfer with meta learning. arXiv:2003.02739 [cs.CL]

[49]

Walker Orr, Prasad Tadepalli, and Xiaoli Fern. 2018. Event detection with neural networks: A rigorous empirical evaluation. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, Brussels, Belgium, 999–1004. DOI:

[50]

Cheonbok Park, Yunwon Tae, Taehee Kim, Soyoung Yang, Mohammad Azam Khan, Eunjeong Park, and Jaegul Choo. 2021. Unsupervised Neural Machine Translation for Low-Resource Domains via Meta-Learning. arXiv:2010.09046 [cs.CL]

[51]

Amir Pouran Ben Veyseh, Viet Dac Lai, Franck Dernoncourt, and Thien Huu Nguyen. 2021. Unleash GPT-2 power for event detection. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). Association for Computational Linguistics, Online, 6271–6282. DOI:

[52]

Kun Qian and Zhou Yu. 2019. Domain adaptive dialog generation via meta learning. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, Florence, Italy, 2639–2649. DOI:

[53]

Yanxia Qin, Zhongqing Wang, Yue Zhang, Kehai Chen, and Min Zhang. 2022. Advancing Chinese event detection via revisiting character information. ACM Transactions on Asian and Low-Resource Language Information Processing 21, 4, Article 78 (Feb2022), 9 pages. DOI:

Digital Library

[54]

Sachin Ravi and Hugo Larochelle. 2017. Optimization as a model for few-shot learning. International Conference on Learning Representations. https://openreview.net/forum?id=rJY0-Kcl.

[55]

Sovan Kumar Sahoo, Saumajit Saha, Asif Ekbal, and Pushpak Bhattacharyya. 2019. A multi-task model for multilingual trigger detection and classification. In Proceedings of the 16th International Conference on Natural Language Processing. NLP Association of India, International Institute of Information Technology, Hyderabad, India, 160–169. https://aclanthology.org/2019.icon-1.19.

[56]

Sovan Kumar Sahoo, Saumajit Saha, Asif Ekbal, and Pushpak Bhattacharyya. 2020. A platform for event extraction in Hindi. In Proceedings of the 12th Language Resources and Evaluation Conference. European Language Resources Association, Marseille, France, 2241–2250. https://aclanthology.org/2020.lrec-1.273.

[57]

Adam Santoro, Sergey Bartunov, Matthew Botvinick, Daan Wierstra, and Timothy Lillicrap. 2016. Meta-learning with memory-augmented neural networks. In International Conference on Machine Learning. PMLR, 1842–1850.

Digital Library

[58]

Jiawei Sheng, Shu Guo, Bowen Yu, Qian Li, Yiming Hei, Lihong Wang, Tingwen Liu, and Hongbo Xu. 2021. CasEE: A joint learning framework with cascade decoding for overlapping event extraction. arXiv:2107.01583 [cs.CL]

[59]

Jake Snell, Kevin Swersky, and Richard S. Zemel. 2017. Prototypical networks for few-shot learning. arXiv preprint arXiv:1703.05175

[60]

Meihan Tong, Shuai Wang, Yixin Cao, Bin Xu, Juanzi Li, Lei Hou, and Tat-Seng Chua. 2020. Image enhanced event detection in news articles. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34. 9040–9047.

[61]

Meihan Tong, Bin Xu, Shuai Wang, Yixin Cao, Lei Hou, Juanzi Li, and Jun Xie. 2020. Improving event detection via open-domain trigger knowledge. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, Online, 5887–5897. DOI:

[62]

Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. arXiv:1706.03762 [cs.CL]

[63]

Oriol Vinyals, Charles Blundell, Timothy Lillicrap, Koray Kavukcuoglu, and Daan Wierstra. 2016. Matching networks for one shot learning. In Proceedings of the 30th International Conference on Neural Information Processing, Vol. 29, Curran Associates Inc., Red Hook, NY, 3637–3645.

[64]

Christopher Walker, Stephanie Strassel, Julie Medero, and Kazuaki Maeda. [n. d.]. ACE 2005 multilingual training corpus ldc2006t06, 2006.

[65]

Xiaozhi Wang, Xu Han, Zhiyuan Liu, Maosong Sun, and Peng Li. 2019. Adversarial training for weakly supervised event detection. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). Association for Computational Linguistics, Minneapolis, MN, 998–1008. DOI:

[66]

Ziqi Wang, Xiaozhi Wang, Xu Han, Yankai Lin, Lei Hou, Zhiyuan Liu, Peng Li, Juanzi Li, and Jie Zhou. 2021. CLEVE: Contrastive Pre-training for Event Extraction. arXiv:2105.14485 [cs.CL]

[67]

Qianhui Wu, Zijia Lin, Guoxin Wang, Hui Chen, Börje F. Karlsson, Biqing Huang, and Chin-Yew Lin. 2020. Enhanced meta-learning for cross-lingual named entity recognition with minimal resources. In 34th AAAI Conference on Artificial Intelligence (AAAI’20). AAAI Press, 9274–9281. https://www.microsoft.com/en-us/research/publication/enhanced-meta-learning-for-cross-lingual-named-entity-recognition-with-minimal-resources/.

[68]

Shijie Wu and Mark Dredze. 2019. Beto, Bentz, Becas: The surprising cross-lingual effectiveness of BERT. arXiv:1904.09077 [cs.CL]

[69]

Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V. Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, Jeff Klingner, Apurva Shah, Melvin Johnson, Xiaobing Liu, Łukasz Kaiser, Stephan Gouws, Yoshikiyo Kato, Taku Kudo, Hideto Kazawa, Keith Stevens, George Kurian, Nishant Patil, Wei Wang, Cliff Young, Jason Smith, Jason Riesa, Alex Rudnick, Oriol Vinyals, Greg Corrado, Macduff Hughes, and Jeffrey Dean. 2016. Google’s neural machine translation system: Bridging the gap between human and machine translation. arXiv:1609.08144 [cs.CL]

[70]

Jianye Xie, Haotong Sun, Junsheng Zhou, Weiguang Qu, and Xinyu Dai. 2021. Event detection as graph parsing. In Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021. Association for Computational Linguistics, Online, 1630–1640. DOI:

[71]

Jiateng Xie, Zhilin Yang, Graham Neubig, Noah A. Smith, and Jaime Carbonell. 2018. Neural cross-lingual named entity recognition with minimal resources. arXiv:1808.09861 [cs.CL]

[72]

Runxin Xu, Tianyu Liu, Lei Li, and Baobao Chang. 2021. Document-level event extraction via heterogeneous graph-based interaction model with a tracker. arXiv:2105.14924 [cs.CL]

[73]

Haoran Yan, Xiaolong Jin, Xiangbin Meng, Jiafeng Guo, and Xueqi Cheng. 2019. Event detection with multi-order graph convolution and aggregated attention. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, 5766–5770. DOI:

[74]

Sen Yang, Dawei Feng, Linbo Qiao, Zhigang Kan, and Dongsheng Li. 2019. Exploring pre-trained language models for event extraction and generation. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, Florence, Italy, 5284–5294. DOI:

[75]

Yi Yang and Arzoo Katiyar. 2020. Simple and effective few-shot named entity recognition with structured nearest neighbor learning. arXiv preprint arXiv:2010.02405

[76]

Zhu Zhu, Shoushan Li, Guodong Zhou, and Rui Xia. 2014. Bilingual event extraction: A case study on trigger type determination. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). Association for Computational Linguistics, Baltimore, MD, 842–847. DOI:

Cited By

Sun HKong B(2024)Sustainable improvement and application of multilingual english translation quality using T5 and MAMLDiscover Artificial Intelligence10.1007/s44163-024-00213-54:1Online publication date: 2-Dec-2024
https://doi.org/10.1007/s44163-024-00213-5
Rahali AAkhloufi M(2023)End-to-End Transformer-Based Models in Textual-Based NLPAI10.3390/ai40100044:1(54-110)Online publication date: 5-Jan-2023
https://doi.org/10.3390/ai4010004

Index Terms

Meta-ED: Cross-lingual Event Detection Using Meta-learning for Indian Languages
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Information extraction

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Published In

cover image ACM Transactions on Asian and Low-Resource Language Information Processing

ACM Transactions on Asian and Low-Resource Language Information Processing Volume 22, Issue 2

February 2023

624 pages

ISSN:2375-4699

EISSN:2375-4702

DOI:10.1145/3572719

Editor:
Imed Zitouni
Google, USA

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Publication History

Published: 21 February 2023

Online AM: 09 August 2022

Accepted: 04 July 2022

Revised: 20 June 2022

Received: 06 April 2022

Published in TALLIP Volume 22, Issue 2

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Cited By

Sun HKong B(2024)Sustainable improvement and application of multilingual english translation quality using T5 and MAMLDiscover Artificial Intelligence10.1007/s44163-024-00213-54:1Online publication date: 2-Dec-2024
https://doi.org/10.1007/s44163-024-00213-5
Rahali AAkhloufi M(2023)End-to-End Transformer-Based Models in Textual-Based NLPAI10.3390/ai40100044:1(54-110)Online publication date: 5-Jan-2023
https://doi.org/10.3390/ai4010004

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