Abstract
Despite the wide use of inline formatting, not much has been studied on translating sentences with inline formatted tags. The detag-and-project approach using word alignments is one solution to translating a tagged sentence. However, the method has a limitation: tag reinsertion is not considered in the translation process. Another solution is to use an end-to-end model which takes text with inline tags as inputs and translates them into a tagged sentence. This approach can alleviate the problems of the aforementioned method, but there is no sufficient parallel corpus dedicated to such a task. To solve this problem, an automatic data augmentation method by tag injection is suggested, but it is computationally expensive and augmentation is limited since the model is based on isolated translation for all fragments. In this paper, we propose an efficient and effective tag augmentation method based on word alignment. Our experiments show that our approach outperforms the detag-and-project methods. We also introduce a metric to evaluate the placement of tags and show that the suggested metric is reasonable for our task. We further analyze the effectiveness of each implementation detail.- Anthology ID:
- 2022.wmt-1.81
- Volume:
- Proceedings of the Seventh Conference on Machine Translation (WMT)
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates (Hybrid)
- Editors:
- Philipp Koehn, Loïc Barrault, Ondřej Bojar, Fethi Bougares, Rajen Chatterjee, Marta R. Costa-jussà, Christian Federmann, Mark Fishel, Alexander Fraser, Markus Freitag, Yvette Graham, Roman Grundkiewicz, Paco Guzman, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Tom Kocmi, André Martins, Makoto Morishita, Christof Monz, Masaaki Nagata, Toshiaki Nakazawa, Matteo Negri, Aurélie Névéol, Mariana Neves, Martin Popel, Marco Turchi, Marcos Zampieri
- Venue:
- WMT
- SIG:
- SIGMT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 886–894
- Language:
- URL:
- https://aclanthology.org/2022.wmt-1.81
- DOI:
- Cite (ACL):
- Yonghyun Ryu, Yoonjung Choi, and Sangha Kim. 2022. Data Augmentation for Inline Tag-Aware Neural Machine Translation. In Proceedings of the Seventh Conference on Machine Translation (WMT), pages 886–894, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.
- Cite (Informal):
- Data Augmentation for Inline Tag-Aware Neural Machine Translation (Ryu et al., WMT 2022)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/2022.wmt-1.81.pdf