1. Review of neural approaches for conditional text generation
- Author
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A. A. Marchenko and O. H. Skurzhanskyi
- Subjects
Artificial neural network ,Computer science ,Text simplification ,business.industry ,media_common.quotation_subject ,02 engineering and technology ,General Medicine ,Transduction (psychology) ,computer.software_genre ,Paraphrase ,Spelling ,Task (project management) ,03 medical and health sciences ,0302 clinical medicine ,030221 ophthalmology & optometry ,0202 electrical engineering, electronic engineering, information engineering ,Key (cryptography) ,020201 artificial intelligence & image processing ,Quality (business) ,Artificial intelligence ,business ,computer ,Natural language processing ,media_common - Abstract
The article is devoted to the review of conditional test generation, one of the most promising fields of natural language processing and artificial intelligence. Specifically, we explore monolingual local sequence transduction tasks: paraphrase generation, grammatical and spelling errors correction, text simplification. To give a better understanding of the considered tasks, we show examples of good rewrites. Then we take a deep look at such key aspects as publicly available datasets with the splits (training, validation, and testing), quality metrics for proper evaluation, and modern solutions based primarily on modern neural networks. For each task, we analyze its main characteristics and how they influence the state-of-the-art models. Eventually, we investigate the most significant shared features for the whole group of tasks in general and for approaches that provide solutions for them. Pages of the article in the issue: 102 - 107 Language of the article: Ukrainian
- Published
- 2021
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