Building an Arabic Machine Translation Post-Edited Corpus: Guidelines and Annotation

By W. Zaghouani, Nizar Habash, Ossama Obeid, Behrang Mohit, Houda Bouamor, Kemal Oflazer. 2016

19 citations recorded by the source corpus.

We present our guidelines and annotation procedure to create a human corrected machine translated post-edited corpus for the Modern Standard Arabic. Our overarching goal is to use the annotated corpus to develop automatic machine translation post-editing systems for Arabic that can be used to help accelerate the human revision process of translated texts. The creation of any manually annotated corpus usually presents many challenges. In order to address these challenges, we created comprehensive and simplified annotation guidelines which were used by a team of five annotators and one lead annotator. In order to ensure a high annotation agreement between the annotators, multiple training sessions were held and regular inter-annotator agreement measures were performed to check the annotation quality. The created corpus of manual post-edited translations of English to Arabic articles is the largest to date for this language pair.

Subfields: Artificial Intelligence, Evaluation & Benchmarks, Machine Translation

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Citations in the corpus

VaTeX: A Large-Scale, High-Quality Multilingual Dataset for Video-and-Language Research (2019, methodology): “Thus, following previous methods [8, 65] on collecting parallel pairs, we choose the post-editing annotation strategy.”