Abstract
Even with the latest developments in deep learning and large-scale language modeling, the task of machine translation (MT) of low-resource languages remains a challenge. Neural MT systems can be trained in an unsupervised way without any translation resources but the quality lags behind, especially in truly low-resource conditions. We propose a training strategy that relies on pseudo-parallel sentence pairs mined from monolingual corpora in addition to synthetic sentence pairs back-translated from monolingual corpora. We experiment with different training schedules and reach an improvement of up to 14.5 BLEU points (English to Ukrainian) over a baseline trained on back-translated data only.- Anthology ID:
- 2023.mtsummit-research.12
- Volume:
- Proceedings of Machine Translation Summit XIX, Vol. 1: Research Track
- Month:
- September
- Year:
- 2023
- Address:
- Macau SAR, China
- Editors:
- Masao Utiyama, Rui Wang
- Venue:
- MTSummit
- SIG:
- Publisher:
- Asia-Pacific Association for Machine Translation
- Note:
- Pages:
- 135–147
- Language:
- URL:
- https://aclanthology.org/2023.mtsummit-research.12
- DOI:
- Cite (ACL):
- Ivana Kvapilíková and Ondřej Bojar. 2023. Boosting Unsupervised Machine Translation with Pseudo-Parallel Data. In Proceedings of Machine Translation Summit XIX, Vol. 1: Research Track, pages 135–147, Macau SAR, China. Asia-Pacific Association for Machine Translation.
- Cite (Informal):
- Boosting Unsupervised Machine Translation with Pseudo-Parallel Data (Kvapilíková & Bojar, MTSummit 2023)
- PDF:
- https://preview.aclanthology.org/naacl24-info/2023.mtsummit-research.12.pdf