Neural Text Question Generation for Russian Language Using Hybrid Intelligent Information Systems Approach
| Autor: | Yuriy E. Gapanyuk, Marina Belyanova, Ark M. Andreev |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
business.industry
Computer science media_common.quotation_subject computer.software_genre Task (project management) Autoregressive model ComputingMethodologies_DOCUMENTANDTEXTPROCESSING Information system Quality (business) Artificial intelligence Architecture business On Language computer Natural language processing Natural language Generative grammar media_common |
| Zdroj: | Advances in Neural Computation, Machine Learning, and Cognitive Research V ISBN: 9783030915803 |
| DOI: | 10.1007/978-3-030-91581-0_29 |
| Popis: | The paper considers the task of natural language text generation, particularly the task of question generation based on the given text. Recent research in the area of text generation shows that better quality is achieved using the autoregressive generative models trained on language modeling tasks. However, such models do not use the metainformation of the text structure. In the paper, this approach is applied to Russian language texts. The architecture of the intelligent question generation system is described in detail. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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