Abstract
Neural Machine Translation (NMT) has now attained state-of-art performance on large-scale data. However, it does not achieve the best translation results on small data sets. Example-Based Machine Translation (EBMT) is an approach to machine translation in which existing examples in a database are retrieved and modified to generate new translations. To combine EBMT with NMT, an architecture based on the Transformer model is proposed. We conduct two experiments respectively using limited amounts of data, one on an English-French bilingual dataset and the other one on a multilingual dataset with six languages (English, French, German, Chinese, Japanese and Russian). On the bilingual task, our method achieves an accuracy of 96.5 and a BLEU score of 98.8. On the multilingual task, it also outperforms OpenNMT in terms of BLEU scores.- Anthology ID:
- 2023.clasp-1.9
- Volume:
- Proceedings of the 2023 CLASP Conference on Learning with Small Data (LSD)
- Month:
- September
- Year:
- 2023
- Address:
- Gothenburg, Sweden
- Editors:
- Ellen Breitholtz, Shalom Lappin, Sharid Loaiciga, Nikolai Ilinykh, Simon Dobnik
- Venue:
- CLASP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 72–80
- Language:
- URL:
- https://aclanthology.org/2023.clasp-1.9
- DOI:
- Cite (ACL):
- Haozhe Xiao, Yifei Zhou, and Yves Lepage. 2023. Example-Based Machine Translation with a Multi-Sentence Construction Transformer Architecture. In Proceedings of the 2023 CLASP Conference on Learning with Small Data (LSD), pages 72–80, Gothenburg, Sweden. Association for Computational Linguistics.
- Cite (Informal):
- Example-Based Machine Translation with a Multi-Sentence Construction Transformer Architecture (Xiao et al., CLASP 2023)
- PDF:
- https://preview.aclanthology.org/corrections-2024-07/2023.clasp-1.9.pdf