Generating Natural Language Questions Using Neural Networks
( Pp. 235-239)
More about authors
Malekova Victoria A.
Deputy head of department, Department of Data Analysis and Machine Learning
Financial University under the Government of the Russian Federation
Moscow, Russian Federation Romanova Ekaterina V. Cand. Sci. (Phys.-Math.), Associate Professor, Deputy head of department for scientific work, Department of Data Analysis and Machine Learning
Financial University under the Government of the Russian Federation
Moscow, Russian Federation
Financial University under the Government of the Russian Federation
Moscow, Russian Federation Romanova Ekaterina V. Cand. Sci. (Phys.-Math.), Associate Professor, Deputy head of department for scientific work, Department of Data Analysis and Machine Learning
Financial University under the Government of the Russian Federation
Moscow, Russian Federation
Abstract:
The article demonstrates the main known algorithms for autonomous generation of questions in natural language using neural network tools. Various methods for solving emerging difficulties, the mechanism of the model and the ways of implementation are considered. The results of applying the main algorithms and their analysis to improve the chosen method are presented.
How to Cite:
Malekova V.A., Romanova E.V., (2022), GENERATING NATURAL LANGUAGE QUESTIONS USING NEURAL NETWORKS. Economic Problems and Legal Practice, 2 => 235-239.
Reference list:
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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).
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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).
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).
Keywords:
neural networks, natural language, generation, analysis, neural network model.
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