%0 Conference Proceedings %T Automatic Bilingual Markup Transfer %A Zenkel, Thomas %A Wuebker, Joern %A DeNero, John %Y Moens, Marie-Francine %Y Huang, Xuanjing %Y Specia, Lucia %Y Yih, Scott Wen-tau %S Findings of the Association for Computational Linguistics: EMNLP 2021 %D 2021 %8 November %I Association for Computational Linguistics %C Punta Cana, Dominican Republic %F zenkel-etal-2021-automatic-bilingual %X We describe the task of bilingual markup transfer, which involves placing markup tags from a source sentence into a fixed target translation. This task arises in practice when a human translator generates the target translation without markup, and then the system infers the placement of markup tags. This task contrasts from previous work in which markup transfer is performed jointly with machine translation. We propose two novel metrics and evaluate several approaches based on unsupervised word alignments as well as a supervised neural sequence-to-sequence model. Our best approach achieves an average accuracy of 94.7% across six language pairs, indicating its potential usefulness for real-world localization tasks. %R 10.18653/v1/2021.findings-emnlp.299 %U https://aclanthology.org/2021.findings-emnlp.299 %U https://doi.org/10.18653/v1/2021.findings-emnlp.299 %P 3524-3533