Neural approaches for writing assistant tasks

Authors

  • O. H. Skurzhanskyi Taras Shevchenko National University of Kyiv https://orcid.org/0000-0001-7391-045X
  • A. A. Marchenko Taras Shevchenko National University of Kyiv

DOI:

https://doi.org/10.17721/1812-5409.2023/2.40

Keywords:

natural language processing, neural networks, machine learning, writing assistant, paraphrasing, grammar correction, text simplification

Abstract

The article is devoted to the analysis of tasks for building a writing assistant, one of the most prominent fields of natural language processing and artificial intelligence in general. Specifically, we explore monolingual local sequence transduction tasks: grammatical and spelling errors correction, text simplification, paraphrase generation. To give a better understanding of the considered tasks, we show examples of expected rewrites. Then we take a deep look at such key aspects as existing publicly available datasets and their training splits, quality metrics for high quality evaluation, and modern solutions based primarily on neural networks. For each task, we analyze its main peculiarities and how they influence the state-of-the-art models. Eventually, we investigate the most eloquent shared features for the whole group of tasks in general and for approaches that provide solutions to them.

Pages of the article in the issue: 232 - 238

Language of the article: Ukrainian

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Published

2023-12-23

How to Cite

Skurzhanskyi, O. H., & Marchenko, A. A. (2023). Neural approaches for writing assistant tasks. Bulletin of Taras Shevchenko National University of Kyiv. Physical and Mathematical Sciences, (2), 232–238. https://doi.org/10.17721/1812-5409.2023/2.40

Issue

Section

Computer Science and Informatics