We introduce Spivavtor, a dataset, and instruction-tuned models for text
editing focused on the Ukrainian language. Spivavtor is the Ukrainian-focused
adaptation of the English-only CoEdIT model. Similar to CoEdIT, Spivavtor
performs text editing tasks by following instructions in Ukrainian. This paper
describes the details of the Spivavtor-Instruct dataset and Spivavtor models.
We evaluate Spivavtor on a variety of text editing tasks in Ukrainian, such as
Grammatical Error Correction (GEC), Text Simplification, Coherence, and
Paraphrasing, and demonstrate its superior performance on all of them. We
publicly release our best-performing models and data as resources to the
community to advance further research in this space.

介绍了 Spivavtor，这是一个基于乌克兰语的文本编辑数据集和指导模型。Spivavtor 在乌克兰语中通过遵循指令进行文本编辑任务，通过评估在多种乌克兰语的文本编辑任务中的性能表现，如语法错误纠正、文本简化、连贯性和改写，并且展示其在所有任务上的卓越性能。我们公开发布了我们性能最佳的模型和数据，以促进该领域的进一步研究。

Spivavtor: 一种针对乌克兰文本编辑的指令调整模型

Spivavtor: An Instruction Tuned Ukrainian Text Editing Model

Despite the extensive amount of labeled datasets in the NLP text
classification field, the persistent imbalance in data availability across
various languages remains evident. Ukrainian, in particular, stands as a
language that still can benefit from the continued refinement of cross-lingual
methodologies. Due to our knowledge, there is a tremendous lack of Ukrainian
corpora for typical text classification tasks. In this work, we leverage the
state-of-the-art advances in NLP, exploring cross-lingual knowledge transfer
methods avoiding manual data curation: large multilingual encoders and
translation systems, LLMs, and language adapters. We test the approaches on
three text classification tasks -- toxicity classification, formality
classification, and natural language inference -- providing the "recipe" for
the optimal setups.

我们利用 NLP 的最新进展，探索跨语言知识转移方法，避免手动数据整理，以改善 NLP 文本分类领域中各种语言之间数据可用性的不平衡问题，并在三个文本分类任务上进行测试，提供最佳设置的 “配方”。

乌克兰文本分类：跨语言知识迁移方法的探索

Ukrainian Texts Classification: Exploration of Cross-lingual Knowledge  Transfer Approaches

In this paper, we examine the development and usage of six low-resource
machine translation systems translating between the Ukrainian language and each
of the official languages of the Baltic states. We developed these systems in
reaction to the escalating Ukrainian refugee crisis caused by the Russian
military aggression in Ukraine in the hope that they might be helpful for
refugees and public administrations. Now, two months after MT systems were made
public, we analyze their usage patterns and statistics. Our findings show that
the Latvian-Ukrainian and Lithuanian-Ukrainian systems are integrated into the
public services of Baltic states, leading to more than 127 million translated
sentences for the Lithuanian-Ukrainian system. Motivated by these findings, we
further enhance our MT systems by better Ukrainian toponym translation and
publish an improved version of the Lithuanian-Ukrainian system.

本文研究了低资源机器翻译系统在乌克兰语和波罗的海国家官方语言之间的应用，发现拉脱维亚 - 乌克兰语和立陶宛 - 乌克兰语系统被整合到波罗的海国家的公共服务中，Lithuanian-Ukrainian 系统的翻译句子数超过 1.27 亿，并提升了乌克兰地名翻译的能力，发布了 Lithuanian-Ukrainian 系统的改进版。