# JParaCrawl: A Large Scale Web-Based English-Japanese Parallel Corpus

arXiv: https://arxiv.org/abs/1911.10668 · HTML: https://mlsanity.com/paper/1911.10668/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Nov 2019.

## Built on by (most cited first, 21 of 21)

- [Glot500: Scaling Multilingual Corpora and Language Models to 500 Languages](https://mlsanity.com/paper/2305.12182/) (2023)
- [When Does Translation Require Context? A Data-driven, Multilingual Exploration](https://mlsanity.com/paper/2109.07446/) (2021)
- [Goldfish: Monolingual Language Models for 350 Languages](https://mlsanity.com/paper/2408.10441/) (2024)
- [CoCoA-MT: A Dataset and Benchmark for Contrastive Controlled MT with Application to Formality](https://mlsanity.com/paper/2205.04022/) (2022)
- [GenTranslate: Large Language Models are Generative Multilingual Speech and Machine Translators](https://mlsanity.com/paper/2402.06894/) (2024)
- [JParaCrawl v3.0: A Large-scale English-Japanese Parallel Corpus](https://mlsanity.com/paper/2202.12607/) (2022)
- [EMMA-500: Enhancing Massively Multilingual Adaptation of Large Language Models](https://mlsanity.com/paper/2409.17892/) (2024)
- [Controlling Translation Formality Using Pre-trained Multilingual Language Models](https://mlsanity.com/paper/2205.06644/) (2022)
- [Improving Simultaneous Machine Translation with Monolingual Data](https://mlsanity.com/paper/2212.01188/) (2022)
- [JaSPICE: Automatic Evaluation Metric Using Predicate-Argument Structures for Image Captioning Models](https://mlsanity.com/paper/2311.04192/) (2023)
- [MOLE: Metadata Extraction and Validation in Scientific Papers Using LLMs](https://mlsanity.com/paper/2505.19800/) (2025)
- [Input Augmentation Improves Constrained Beam Search for Neural Machine Translation: NTT at WAT 2021](https://mlsanity.com/paper/2106.05450/) (2021)
- [Sockeye 3: Fast Neural Machine Translation with PyTorch](https://mlsanity.com/paper/2207.05851/) (2022)
- [Domain Adaptation of Machine Translation with Crowdworkers](https://mlsanity.com/paper/2210.15861/) (2022)
- [Revisiting Context Choices for Context-aware Machine Translation](https://mlsanity.com/paper/2109.02995/) (2021)
- [Lightweight Audio Segmentation for Long-form Speech Translation](https://mlsanity.com/paper/2406.10549/) (2024)
- [Static Word Embeddings for Sentence Semantic Representation](https://mlsanity.com/paper/2506.04624/) (2025)
- [JP-TL-Bench: Anchored Pairwise LLM Evaluation for Bidirectional Japanese-English Translation](https://mlsanity.com/paper/2601.00223/) (2026)
- [Media of Langue: The Interface for Exploring Word Translation Network/Space](https://mlsanity.com/paper/2309.08609/) (2023)
- [A Japanese-Chinese Parallel Corpus Using Crowdsourcing for Web Mining](https://mlsanity.com/paper/2405.09017/) (2024)
- [CAT-Translate: Building Compact Open-Source Models for Japanese-English Translation](https://mlsanity.com/paper/2606.21413/) (2026)

## Abstract (arXiv)

Recent machine translation algorithms mainly rely on parallel corpora. However, since the availability of parallel corpora remains limited, only some resource-rich language pairs can benefit from them. We constructed a parallel corpus for English-Japanese, for which the amount of publicly available parallel corpora is still limited. We constructed the parallel corpus by broadly crawling the web and automatically aligning parallel sentences. Our collected corpus, called JParaCrawl, amassed over 8.7 million sentence pairs. We show how it includes a broader range of domains and how a neural machine translation model trained with it works as a good pre-trained model for fine-tuning specific domains. The pre-training and fine-tuning approaches achieved or surpassed performance comparable to model training from the initial state and reduced the training time. Additionally, we trained the model with an in-domain dataset and JParaCrawl to show how we achieved the best performance with them. JParaCrawl and the pre-trained models are freely available online for research purposes.