mlsanityData to 14 Oct 2026

Samsung R&D Institute Philippines at WMT 2023

arXiv 2310.16322 ↗First posted 25 Oct 2023cs.CLcited by 0

Not on the map. Why?

Abstract (arXiv)

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: enrightarrowhe and herightarrowen. 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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 14 of its 18 references that are in the map. First posted on arXiv 25 Oct 2023.

Builds on

14 of 18 references.

References · 14

  1. 2017Attention Is All You Needcited by 60,361
  2. 2015Neural Machine Translation of Rare Words with Subword Unitscited by 2,918
  3. 2018SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processingcited by 1,989
  4. 2018A Call for Clarity in Reporting BLEU Scorescited by 1,799
  5. 2019fairseq: A Fast, Extensible Toolkit for Sequence Modelingcited by 1,488
  6. 2022No Language Left Behind: Scaling Human-Centered Machine Translationcited by 1,312
  7. 2015Improving Neural Machine Translation Models with Monolingual Datacited by 788
  8. 2020Language-agnostic BERT Sentence Embeddingcited by 627
  9. 2020Multilingual Translation with Extensible Multilingual Pretraining and Finetuningcited by 287
  10. 2019Simple and Effective Noisy Channel Modeling for Neural Machine Translationcited by 37
  11. 2018NCRF++: An Open-source Neural Sequence Labeling Toolkitcited by 29
  12. 2020Neural Modeling for Named Entities and Morphology (NEMO^2)cited by 26
2 more
  1. 2021Improving Large-scale Language Models and Resources for Filipinocited by 20
  2. 2021Data Processing Matters: SRPH-Konvergen AI's Machine Translation System for WMT'21cited by 2

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2023Accelerating Transformer Inference for Translation via Parallel Decoding
  2. 2025Beyond Decoder-only: Large Language Models Can be Good Encoders for Machine Translation
  3. 2024Domain-Specific Translation with Open-Source Large Language Models: Resource-Oriented Analysis
  4. 2024A Thorough Examination of Decoding Methods in the Era of LLMs
  5. 2024Think Big, Generate Quick: LLM-to-SLM for Fast Autoregressive Decoding
  6. 2021The Volctrans GLAT System: Non-autoregressive Translation Meets WMT21
  7. 2023Speech Translation with Foundation Models and Optimal Transport: UPC at IWSLT23
  8. 2023MobileNMT: Enabling Translation in 15MB and 30ms