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Data-Driven Approach for Formality-Sensitive Machine Translation: Language-Specific Handling and Synthetic Data Generation

arXiv 2306.14514 ↗First posted 26 Jun 2023cs.CL cs.AIcited by 0

On the map: Synthetic data and self-improvement for LLMs → LLM synthetic data generation

Abstract (arXiv)

In this paper, we introduce a data-driven approach for Formality-Sensitive Machine Translation (FSMT) that caters to the unique linguistic properties of four target languages. Our methodology centers on two core strategies: 1) language-specific data handling, and 2) synthetic data generation using large-scale language models and empirical prompt engineering. This approach demonstrates a considerable improvement over the baseline, highlighting the effectiveness of data-centric techniques. Our prompt engineering strategy further improves performance by producing superior synthetic translation examples.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in LLM synthetic data generation, one of 13 directions in Synthetic data and self-improvement for LLMs. Builds on 11 of its 16 references that are in the map. First posted on arXiv 26 Jun 2023.

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11 of 16 references.

References · 11

  1. 2016Enriching Word Vectors with Subword Informationcited by 1,583
  2. 2020Multilingual Denoising Pre-training for Neural Machine Translationcited by 1,035
  3. 2019CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Datacited by 548
  4. 2016Transfer Learning for Low-Resource Neural Machine Translationcited by 188
  5. 2019Neural Machine Translation: A Review and Surveycited by 100
  6. 2017Massive Exploration of Neural Machine Translation Architecturescited by 55
  7. 2020An Empirical Study of Tokenization Strategies for Various Korean NLP Taskscited by 33
  8. 2022CoCoA-MT: A Dataset and Benchmark for Contrastive Controlled MT with Application to Formalitycited by 24
  9. 2022Controlling Translation Formality Using Pre-trained Multilingual Language Modelscited by 14
  10. 2021PhoMT: A High-Quality and Large-Scale Benchmark Dataset for Vietnamese-English Machine Translationcited by 12
  11. 2022MTet: Multi-domain Translation for English and Vietnamesecited by 5

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