S amsung R & D Institute P hilippines at WMT 2023 (2023)

Abstract: In this paper, we describe the constrained submission systems of Samsung R&D Institute Philippines to the WMT 2023 General Translation Task for two directions: en->he and he->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.

## introduction this paper describes samsung r&d institute philippines's submission to the wmt 2023 general translation task. we participate in two translation directions: @xmath0, submitting two constrained single-direction models based on the transformer @xcite sequence-tosequence architecture. we employ a number of best practices, using a comprehensive data preprocessing pipeline to ensure parallel data quality, create synthetic data through carefully-curated backtranslation, and use reranking methods to select the best candidate translations. our systems achieve strong performance on public benchmarks: 44.24 bleu and 33.77 bleu for flores-200 and ntrex-128 @xmath1, respectively, and; 42.42 bleu and 36.89 bleu on flores-200 and ntrex-128 @xmath2, respectively. our systems outperform mbart50 m2m and slightly underperform against nllb 200 moe despite having significantly less parameters compared to these unconstrained baselines. we detail our data preprocessing, model training, data augmentation, and translation methodology. additionally, we illustrate hyperparameter sweeping setups and study the effects of hyperparameters during online decoding with reranking. ## results a summary of our results on benchmarks can be found on ## conclusion in this paper, we describe our submissions to the wmt 2023 general translation task. we participate in two constrained tracks: @xmath3. we submit two monodirectional models based on the transformer architecture. both models are trained using a mix of original and synthetic backtranslated data, filtered and curated using a comprehensive data processing pipeline that combines embedding-based, heuristic-based, and ratio-based filters. additionally, we employ noisy channel reranking to improve translation candidates using a language model and a channel model trained in the opposite direction. on two benchmark datasets, our systems outperform mbart50 m2m and perform slightly worse than nllb 200 moe, both unconstrained systems with significantly more parameters. our results show that established best practices still perform strongly on constrained systems without the need for extraneous data sources as is with unconstrained systems for the same translation directions.