Review of neural approaches for conditional text generation
DOI:
https://doi.org/10.17721/1812-5409.2021/1.13Keywords:
natural language processing, neural networks, machine learning, conditional text generation, paraphrase generation, grammatical error correction, text simplificationAbstract
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
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