mlsanityData to 7 Oct 2026

JParaCrawl v3.0: A Large-scale English-Japanese Parallel Corpus

arXiv 2202.12607 ↗First posted 25 Feb 2022cs.CLcited by 17

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Abstract (arXiv)

Most current machine translation models are mainly trained with parallel corpora, and their translation accuracy largely depends on the quality and quantity of the corpora. Although there are billions of parallel sentences for a few language pairs, effectively dealing with most language pairs is difficult due to a lack of publicly available parallel corpora. This paper creates a large parallel corpus for English-Japanese, a language pair for which only limited resources are available, compared to such resource-rich languages as English-German. It introduces a new web-based English-Japanese parallel corpus named JParaCrawl v3.0. Our new corpus contains more than 21 million unique parallel sentence pairs, which is more than twice as many as the previous JParaCrawl v2.0 corpus. Through experiments, we empirically show how our new corpus boosts the accuracy of machine translation models on various domains. The JParaCrawl v3.0 corpus will eventually be publicly available online for research purposes.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). Builds on 17 of its 36 references that are in the map. First posted on arXiv 25 Feb 2022.

Builds on

17 of 36 references.

References · 17

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5 more
  1. 2019CCMatrix: Mining Billions of High-Quality Parallel Sentences on the WEBcited by 177
  2. 2019JParaCrawl: A Large Scale Web-Based English-Japanese Parallel Corpuscited by 21
  3. 2019Findings of the First Shared Task on Machine Translation Robustnesscited by 17
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 8; 2025: 5; 2026: 3.

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  1. 2024Continual Pre-Training for Cross-Lingual LLM Adaptation: Enhancing Japanese Language Capabilitiescited by 111
  2. 2024Towards Fast Multilingual LLM Inference: Speculative Decoding and Specialized Drafterscited by 12
  3. 2023KIT's Multilingual Speech Translation System for IWSLT 2023cited by 7
  4. 2024Enhancing Translation Accuracy of Large Language Models through Continual Pre-Training on Parallel Datacited by 7
  5. 2026The Finetuner's Fallacy: When to Pretrain with Your Finetuning Datacited by 7
  6. 2024Simplifying Translations for Children: Iterative Simplification Considering Age of Acquisition with LLMscited by 5
  7. 2024Simultaneous Interpretation Corpus Construction by Large Language Models in Distant Language Paircited by 3
  8. 2026Multi-Drafter Speculative Decoding with Alignment Feedbackcited by 3
  9. 2024Generating Diverse Translation with Perturbed kNN-MTcited by 2
  10. 2024Domain adapted machine translation: What does catastrophic forgetting forget and why?cited by 2
  11. 2025BiMax: Bidirectional MaxSim Score for Document-Level Alignmentcited by 2
  12. 2025Static Word Embeddings for Sentence Semantic Representationcited by 1
5 more
  1. 2025JaParaPat: A Large-Scale Japanese-English Parallel Patent Application Corpuscited by 1
  2. 2025Hacking Neural Evaluation Metrics with Single Hub Textcited by 1
  3. 2024A Japanese-Chinese Parallel Corpus Using Crowdsourcing for Web Miningcited by 0
  4. 2025Tracing Multilingual Knowledge Acquisition Dynamics in Domain Adaptation: A Case Study of English-Japanese Biomedical Adaptationcited by 0
  5. 2026CAT-Translate: Building Compact Open-Source Models for Japanese-English Translationcited by 0