  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.
