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.

翻译：文本编辑任务，包括句子融合、句子拆分与改写、文本简化以及语法纠错，其共同特点是处理高度相似的输入和输出序列。该研究领域位于两个成熟领域的交叉点：（i）常用于神经机器翻译等任务的全自回归序列到序列方法；（ii）常用于处理词性标注、命名实体识别等任务的序列标注技术。为寻求平衡的架构，研究人员提出了许多富有想象力和非常规的解决方案，相关讨论见相关工作部分。我们解决文本编辑任务的方法称为RedPenNet，旨在减少特定序列到编辑模型中存在的架构和参数冗余，同时保留其半自回归优势。我们的模型在BEA-2019（测试集）上取得了77.60的$F_{0.5}$分数（除系统组合外可视为最先进水平），在UAGEC+Fluency（测试集）基准上达到67.71。本研究在UNLP 2023研讨会的背景下进行，该研讨会以论文形式提交了乌克兰语语法纠错共享任务。本研究旨在将RedPenNet方法应用于解决乌克兰语的语法纠错问题。