LangMark: 一种多语言数据集用于自动后编辑
LangMark: A Multilingual Dataset for Automatic Post-Editing
AI总结：
LangMark是一个多语言数据集，用于评估和改进自动后编辑系统，通过大规模标注数据展示大型语言模型在后编辑任务中的有效性。
摘要：
自动后编辑（APE）旨在纠正机器翻译文本中的错误，提高翻译质量，同时减少人工干预的需要。尽管神经机器翻译（NMT）取得了进展，但有效APE系统的发展受到缺乏大规模多语言数据集的阻碍，这些数据集专门针对NMT输出进行优化。为解决这一差距，我们提出了并发布了LangMark，一个新的人工标注的多语言APE数据集，用于英语到七种语言的翻译：巴西葡萄牙语、法语、德语、意大利语、日语、俄语和西班牙语。该数据集包含206,983个三元组，每个三元组由源段、NMT输出和人类后编辑翻译组成。由专家人类语言学家标注，该数据集兼具语言多样性和规模。利用该数据集，我们实证表明，使用少量示例提示的大型语言模型（LLMs）可以有效执行APE，优于领先的商业和甚至专有机器翻译系统。我们相信，这一新资源将促进未来APE系统的发展和评估。
英文摘要：
Automatic post-editing (APE) aims to correct errors in machine-translated text, enhancing translation quality, while reducing the need for human intervention. Despite advances in neural machine translation (NMT), the development of effective APE systems has been hindered by the lack of large-scale multilingual datasets specifically tailored to NMT outputs. To address this gap, we present and release LangMark, a new human-annotated multilingual APE dataset for English translation to seven languages: Brazilian Portuguese, French, German, Italian, Japanese, Russian, and Spanish. The dataset has 206,983 triplets, with each triplet consisting of a source segment, its NMT output, and a human post-edited translation. Annotated by expert human linguists, our dataset offers both linguistic diversity and scale. Leveraging this dataset, we empirically show that Large Language Models (LLMs) with few-shot prompting can effectively perform APE, improving upon leading commercial and even proprietary machine translation systems. We believe that this new resource will facilitate the future development and evaluation of APE systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
