Abstract
We present the University of Central Florida systems for the LoResMT 2021 Shared Task, participating in the English-Irish and English-Marathi translation pairs. We focused our efforts on constrained track of the task, using transfer learning and subword segmentation to enhance our models given small amounts of training data. Our models achieved the highest BLEU scores on the fully constrained tracks of English-Irish, Irish-English, and Marathi-English with scores of 13.5, 21.3, and 17.9 respectively- Anthology ID:
- 2021.mtsummit-loresmt.13
- Volume:
- Proceedings of the 4th Workshop on Technologies for MT of Low Resource Languages (LoResMT2021)
- Month:
- August
- Year:
- 2021
- Address:
- Virtual
- Venue:
- LoResMT
- SIG:
- Publisher:
- Association for Machine Translation in the Americas
- Note:
- Pages:
- 129–133
- Language:
- URL:
- https://aclanthology.org/2021.mtsummit-loresmt.13
- DOI:
- Cite (ACL):
- William Chen and Brett Fazio. 2021. The UCF Systems for the LoResMT 2021 Machine Translation Shared Task. In Proceedings of the 4th Workshop on Technologies for MT of Low Resource Languages (LoResMT2021), pages 129–133, Virtual. Association for Machine Translation in the Americas.
- Cite (Informal):
- The UCF Systems for the LoResMT 2021 Machine Translation Shared Task (Chen & Fazio, LoResMT 2021)
- PDF:
- https://preview.aclanthology.org/paclic-22-ingestion/2021.mtsummit-loresmt.13.pdf