Automatic Bilingual Markup Transfer

Thomas Zenkel, Joern Wuebker, John DeNero


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
Venues:
EMNLP | 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
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2021.findings-emnlp.299.pdf
Code
 lilt/markup-transfer-scripts +  additional community code