Articles
| Open Access |
https://doi.org/10.37547/ajast/Volume05Issue12-14
Developing A Question-Answering System In The Uzbek Language Based On The Xlm-Roberta Model
Abstract
This article presents the issue of testing the XML-RoBERTa model for generating questions and answers in the Uzbek language. In the study, the XML-RoBERTa model was adapted to the Uzbek language from a dataset consisting of context, question and answer pairs in the Uzbek language and, as a result, a model was developed to generate a fragment of the answer to the user's question from the context. ROUGE, EM (Exact Match) and F1 control metrics were used to determine the performance of the model.
Keywords
XLM-RoBERTa, question-answer system, natural language processing
References
Muhammadjon Mahmudovich, Ochilov Mannon Musinovich, Xolmatov Orzimurod Abjalolovich, Narzullayev Oybek Otabek o‘g‘li. Vektor fazo modeli hamda jumlalar o‘xshashligi o‘lchovlariga asoslangan savol - javob tizimi ishlab chiqish. (2025). digital transformation and artificial intelligence, 3(1),23-30. https://dtai.tsue.uz/index.php/dtai/article/view/v3i14
Liu Y etcs. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Proceedings of NAACL-HLT 2019. Minneapolis, MN, USA. June 2–7, 2019.
Raffel, C. Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer. Proceedings of the 37th International Conference on Machine Learning (ICML 2020).
Raffel, C., Shinn, E., Roberts, A., Lee, K., & Narang, S. Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer. Proceedings of the 37th International Conference on Machine Learning (ICML), Long Beach, CA, USA, June 9–15, 2019.
Devlin, Jacob, Chang, Ming-Wei, Lee, Kenton, Toutanova, Kristina. "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding". October 11, 2018. arXiv:1810.04805v2
Ethayarajh, Kawin, How Contextual are Contextualized Word Representations? Comparing the Geometry of BERT, ELMo, and GPT-2 Embeddings. September 1, 2019. arXiv:1909.00512
Zhang Tianyi, Wu Felix, Katiyar Arzoo, Weinberger Kilian Q, Artzi Yoav. Revisiting Few-sample BERT Fine-tuning, March 11, 2021. arXiv:2006.05987
Victor SANH, Lysandre DEBUT, Julien CHAUMOND, Thomas WOLF. DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter. 1 Mar 2020. arXiv:1910.01108v4
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, OmerLevy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov. RoBERTa: ARobustly Optimized BERT Pretraining Approach. 26 Jul 2019. arXiv:1907.11692v1.
Zhenzhong Lan, Mingda Chen, Piyush Sharma, Google Research Sebastian Goodman, Radu Soricut. ALBERT: A LITE BERT FOR SELF-SUPERVISED LEARNING OF LANGUAGE REPRESENTATIONS. 9 Feb 2020. arXiv:1909.11942v6 [cs.CL]
Goyal N., Chaudhary V., Wenzek G., Guzmán F., Grave E., Ott M., Zettlemoyer L., & Stoyanov V. Unsupervised Cross-lingual Representation Learning at Scale. arXiv preprint arXiv:1911.02116v2 [cs.CL], 8 April 2020.
Goyal, N., Chaudhary, V., Wenzek, G., Guzmán, F., Grave, E., Ott, M., Zettlemoyer, L., & Stoyanov, V. Unsupervised Cross-lingual Representation Learning at Scale. 2020. arXiv:1911.02116v2.
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Copyright (c) 2025 Khujayarov I.Sh., Ochilov M.M., Kholmatov O.A.

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