LangMark: A Multilingual Dataset for Automatic Post-Editing

TL;DR

This paper introduces LangMark, a large multilingual dataset for automatic post-editing, and demonstrates that large language models can effectively improve translation quality using this resource.

Contribution

The paper presents LangMark, a new extensive multilingual APE dataset, and shows that LLMs with few-shot prompting can enhance machine translation outputs.

Findings

LLMs with few-shot prompting improve APE performance

LangMark dataset covers seven languages with over 200,000 triplets

Effective APE results surpass commercial translation systems

Abstract

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.