LangMark: A Multilingual Dataset for Automatic Post-Editing
This addresses the problem of limited data for APE systems, facilitating development and evaluation, though it is incremental as it builds on existing APE and LLM methods.
The authors tackled the lack of large-scale multilingual datasets for automatic post-editing (APE) by releasing LangMark, a human-annotated dataset with 206,983 triplets across seven languages, and showed that LLMs with few-shot prompting can improve upon leading commercial and proprietary machine translation systems.
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.