Abstract
We present a simple yet powerful data augmentation method for boosting Neural Machine Translation (NMT) performance by leveraging information retrieved from a Translation Memory (TM). We propose and test two methods for augmenting NMT training data with fuzzy TM matches. Tests on the DGT-TM data set for two language pairs show consistent and substantial improvements over a range of baseline systems. The results suggest that this method is promising for any translation environment in which a sizeable TM is available and a certain amount of repetition across translations is to be expected, especially considering its ease of implementation.- Anthology ID:
- P19-1175
- Volume:
- Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2019
- Address:
- Florence, Italy
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1800–1809
- Language:
- URL:
- https://aclanthology.org/P19-1175
- DOI:
- 10.18653/v1/P19-1175
- Cite (ACL):
- Bram Bulte and Arda Tezcan. 2019. Neural Fuzzy Repair: Integrating Fuzzy Matches into Neural Machine Translation. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 1800–1809, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- Neural Fuzzy Repair: Integrating Fuzzy Matches into Neural Machine Translation (Bulte & Tezcan, ACL 2019)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/P19-1175.pdf