  In this paper, we describe the constrained MT systems submitted by Samsung
R&D Institute Philippines to the WMT 2023 General Translation Task for two
directions: en$\rightarrow$he and he$\rightarrow$en. Our systems comprise of
Transformer-based sequence-to-sequence models that are trained with a mix of
best practices: comprehensive data preprocessing pipelines, synthetic
backtranslated data, and the use of noisy channel reranking during online
decoding. Our models perform comparably to, and sometimes outperform, strong
baseline unconstrained systems such as mBART50 M2M and NLLB 200 MoE despite
having significantly fewer parameters on two public benchmarks: FLORES-200 and
NTREX-128.
