Abstract
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.- Anthology ID:
- 2021.findings-emnlp.299
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2021
- Month:
- November
- Year:
- 2021
- Address:
- Punta Cana, Dominican Republic
- Venue:
- Findings
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 3524–3533
- Language:
- URL:
- https://aclanthology.org/2021.findings-emnlp.299
- DOI:
- 10.18653/v1/2021.findings-emnlp.299
- Cite (ACL):
- Thomas Zenkel, Joern Wuebker, and John DeNero. 2021. Automatic Bilingual Markup Transfer. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 3524–3533, Punta Cana, Dominican Republic. Association for Computational Linguistics.
- Cite (Informal):
- Automatic Bilingual Markup Transfer (Zenkel et al., Findings 2021)
- PDF:
- https://preview.aclanthology.org/remove-xml-comments/2021.findings-emnlp.299.pdf
- Code
- lilt/markup-transfer-scripts + additional community code