Generating Natural Language Questions Using Neural Networks
- Autores: Malekova V.A.1, Romanova E.V.1
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							Afiliações: 
							- Financial University under the Government of the Russian Federation
 
- Edição: Volume 18, Nº 2 (2022)
- Páginas: 235-239
- Seção: Articles
- URL: https://ogarev-online.ru/2541-8025/article/view/147081
- ID: 147081
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##article.viewOnOriginalSite##Sobre autores
Victoria Malekova
Financial University under the Government of the Russian Federation
														Email: vamalekova@fa.ru
				                					                																			                								Deputy head of department, Department of Data Analysis and Machine Learning				                								Moscow, Russian Federation						
Ekaterina Romanova
Financial University under the Government of the Russian Federation
														Email: ekvromanova@fa.ru
				                					                																			                								Cand. Sci. (Phys.-Math.), Associate Professor, Deputy head of department for scientific work, Department of Data Analysis and Machine Learning				                								Moscow, Russian Federation						
Bibliografia
- Kumar V., Muneeswaran S., Ramakrishnan G., & Li Y.: ParaQG: A System for Generating Questions and Answers from Paragraphs. In: Proceedings of the 2019 EMNLP and the 9th IJCNLP (System Demonstrations), pp. 175-180. ACL, Hong Kong, China, (2019).
- Pennington J., Socher R., Manning C.: Glove: Global Vectors for Word Representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532-1543. ACL, Doha, Qatar, (2014).
- Devlin, J., Chang, M., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. ArXiv.org, https://arxiv.org/abs/1810.04805, last accessed 2021/03/11.
- Zhou Q., Yang N., Wei F., Tan C., Bao H., Zhou M.: Neural Question Generation from Text: A Preliminary Study. In: Proceedings of the National CCF Conference on Natural Language Processing and Chinese Computing, pp. 662-671 Springer, Cham, (2017).
- Rajpurkar P., Zhang J., Lopyrev K., Liang P.: SQuAD: 100,000+ Questions for Machine Comprehension of Text. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2383-2392. ACL, Austin, Texas, USA, (2016).
- Papineni K., Roukos S., Ward T., Zhu WJ.: BLEU: A Method for Automatic Evaluation of Machine Translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311-318. ACL, Philadelphia, Pennsylvania, USA, (2002).
- Lin CY.: ROUGE: A Package for Automatic Evaluation of summaries. In: Proceedings of the ACL Workshop: Text Summarization Braches Out, pp. 74-81. ACL, Barcelona, Spain, (2004).
- Sun X., Liu J., Lyu Y., He W., Ma Y., Wang S.: Answer-focused and Position-aware Neural Question Generation. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 3930-3939. ACL, Brussels, Belgium, (2018).
- Zhao Y., Ni X., Ding Y., & Ke Q.: Paragraph-level Neural Question Generation with Maxout Pointer and Gated Self-Attention Networks. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 3901-3910. ACL, Brussels, Belgium, (2018).
- Song L., Wang Z., Hamza W., Zhang Y., Gildea D.: Leveraging Context Information for Natural Question Generation. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), pp. 569-574. ACL, New Orleans, Louisiana, USA, (2018).
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