The text editing tasks, including sentence fusion, sentence splitting and rephrasing, text simplification, and Grammatical Error Correction (GEC), share a common trait of dealing with highly similar input and output sequences. This area of research lies at the intersection of two well-established fields: (i) fully autoregressive sequence-to-sequence approaches commonly used in tasks like Neural Machine Translation (NMT) and (ii) sequence tagging techniques commonly used to address tasks such as Part-of-speech tagging, Named-entity recognition (NER), and similar. In the pursuit of a balanced architecture, researchers have come up with numerous imaginative and unconventional solutions, which we're discussing in the Related Works section. Our approach to addressing text editing tasks is called RedPenNet and is aimed at reducing architectural and parametric redundancies presented in specific Sequence-To-Edits models, preserving their semi-autoregressive advantages. Our models achieve $F_{0.5}$ scores of 77.60 on the BEA-2019 (test), which can be considered as state-of-the-art the only exception for system combination and 67.71 on the UAGEC+Fluency (test) benchmarks. This research is being conducted in the context of the UNLP 2023 workshop, where it was presented as a paper as a paper for the Shared Task in Grammatical Error Correction (GEC) for Ukrainian. This study aims to apply the RedPenNet approach to address the GEC problem in the Ukrainian language.

我们的研究旨在应用RedPenNet方法解决乌克兰语的语法错误纠正问题，通过减少具体Sequence-To-Edits模型中的架构和参数的冗余，保持半自回归的优势，我们的模型在BEA-2019（测试）上达到了77.60的$F_{0.5}$分数，可以被视为最先进的，而在UAGEC+流利度（测试）基准上达到了67.71的分数。

纠正语法错误的RedPenNet：单词级输出，注意力范围为片段