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Ucf endnote4/15/2023 ![]() 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. We present the University of Central Florida systems for the LoResMT 2021 Shared Task, participating in the English-Irish and English-Marathi translation pairs. ![]() Proceedings of the 4th Workshop on Technologies for MT of Low Resource Languages (LoResMT2021)Īssociation for Machine Translation in the Americas The UCF Systems for the LoResMT 2021 Machine Translation Shared Task Cite (Informal): The UCF Systems for the LoResMT 2021 Machine Translation Shared Task (Chen & Fazio, LoResMT 2021) Copy Citation: BibTeX Markdown MODS XML Endnote More options… PDF: Association for Machine Translation in the Americas. In Proceedings of the 4th Workshop on Technologies for MT of Low Resource Languages (LoResMT2021), pages 129–133, Virtual. The UCF Systems for the LoResMT 2021 Machine Translation Shared Task. ![]() ![]() 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: DOI: Bibkey: chen-fazio-2021-ucf Cite (ACL): William Chen and Brett Fazio. 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. ![]()
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