UvA-MT's Participation in the WMT23 General Translation Shared Task
This work addresses translation efficiency for language pairs like English-Hebrew, but it is incremental as it builds on existing multilingual methods.
The paper tackled bidirectional English-Hebrew machine translation by using a single multilingual model, achieving competitive results comparable to traditional bilingual models in both directions.
This paper describes the UvA-MT's submission to the WMT 2023 shared task on general machine translation. We participate in the constrained track in two directions: English <-> Hebrew. In this competition, we show that by using one model to handle bidirectional tasks, as a minimal setting of Multilingual Machine Translation (MMT), it is possible to achieve comparable results with that of traditional bilingual translation for both directions. By including effective strategies, like back-translation, re-parameterized embedding table, and task-oriented fine-tuning, we obtained competitive final results in the automatic evaluation for both English -> Hebrew and Hebrew -> English directions.