---
title: "JW300: A Wide-Coverage Parallel Corpus for — Agić 2019"
description: "Cross-lingual NLP for low-resource languages depends on parallel text, and the scarcity of such text hampers both training and evaluation."
authors: ["Željko Agić", "Ivan Vulić"]
year: 2019
venue: "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics"
source: "https://aclanthology.org/P19-1310/"
doi: "10.18653/v1/p19-1310"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/agic-2019-jw300-wide-coverage-parallel-corpus/"
---
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# JW300: A Wide-Coverage Parallel Corpus for Low-Resource Languages

**Authors:** [Željko Agić](https://md.research.elamur.ai/free/en/authors/zeljko-agic/), [Ivan Vulić](https://md.research.elamur.ai/free/en/authors/ivan-vulic/) · **Year:** 2019 · **Venue:** Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/P19-1310/) · [PDF](https://aclanthology.org/P19-1310.pdf) · [DOI 10.18653/v1/p19-1310](https://doi.org/10.18653/v1/p19-1310) · [OpenAlex](https://openalex.org/W2949303037)

**Citations:** 199 citations (openalex-snapshot · 2026-10-14) · [174](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
Cross-lingual NLP for low-resource languages depends on parallel text, and the scarcity of such text hampers both training and evaluation. As a result, methods aimed at low-resource languages are often tested only on well-resourced ones. Agić and Vulić address this with JW300, a corpus crawled from every publication on jw.org, mostly the *Watchtower* and *Awake!* magazines. These articles are largely translated from English and share article IDs across languages. The texts were split and tokenized with Polyglot, using a related language's model when no model existed for a language. Sentences within matching articles were aligned with Yasa.

The authors tested the corpus in two ways:
- **Bilingual lexicon induction:** seed dictionaries taken from JW300 let a simple supervised mapping beat a strong unsupervised method.
- **Part-of-speech tagging:** basic multi-source annotation projection clearly outperformed Bible-based projection and nearly matched the more complex DsDs system.

The corpus balances many languages with substantial data per pair, though it carries a known religious bias. This makes it a practical resource for multilingual research and for studying bias in NLP.

## Key points
- **Size:** JW300 covers 343 languages, with about 1.34M articles, roughly 109M sentences and 1.48B tokens. It contains 54,376 aligned language pairs, averaging about 92K parallel sentences per pair.
- **Licensing:** the corpus is free for non-commercial use under the copyright holder's terms, which are close to CC-BY-NC-SA.
- **Lexicon induction setup:** the BLI experiments used EN, ET, HR, MR and MT, fastText embeddings, and IBM1-derived seed dictionaries.
- **Lexicon induction results:** the supervised method (Smith et al., 2017) beat the unsupervised one (Artetxe et al., 2018) on every pair. On the Conneau et al. test set, it scored 0.342 vs. 0.313 MRR for EN-ET and 0.289 vs. 0.261 for EN-HR.
- **POS tagging setup:** taggers were evaluated on 17 languages, with projection built on Efmaral alignments and the Plank et al. (2016) tagger.
- **POS tagging results:** JW300 PROJ reached 80.8% mean accuracy, compared with 73.4 for BIBLE and 81.4 for DsDs. Using 42 source languages from the JW300–UD v2.3 overlap raised it to 81.6.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Natural language processing](https://md.research.elamur.ai/free/en/shelves/nlp/)

Topics: [Part-of-speech tagging](https://md.research.elamur.ai/free/en/topics/part-of-speech-tagging/)

## Cited by

Cited by

174 papers of our library cite it (of the 140673 whose bibliographies we read); 25 of them have a page here, 149 not yet.

- [MasakhaNER: Named Entity Recognition for African Languages](https://md.research.elamur.ai/free/en/papers/adelani-2021-masakhaner-named-entity-recognition-african/)
- [A Few Thousand Translations Go a Long Way! Leveraging Pre-trained Models for African News Translation](https://md.research.elamur.ai/free/en/papers/adelani-2022-few-thousand-translations-go-long/)
- [An Open Dataset and Model for Language Identification](https://md.research.elamur.ai/free/en/papers/burchell-2023-open-dataset-model-language-identification/)
- [Language ID in the Wild: Unexpected Challenges on the Path to a Thousand-Language Web Text Corpus](https://md.research.elamur.ai/free/en/papers/caswell-2020-language-id-wild-unexpected/)
- [IsraParlTweet: The Israeli Parliamentary and Twitter Resource](https://md.research.elamur.ai/free/en/papers/coling-2024-lan-israparltweet-israeli-parliamentary-twitter-resource/)
- [At the Crossroad of Cuneiform and NLP: Challenges for Fine-grained Part-of-speech Tagging](https://md.research.elamur.ai/free/en/papers/coling-2024-smidt-crossroad-cuneiform-nlp-challenges-fine/)
- [AmericasNLI: Evaluating Zero-shot Natural Language Understanding of Pretrained Multilingual Models in Truly Low-resource Languages](https://md.research.elamur.ai/free/en/papers/ebrahimi-2022-americasnli-evaluating-zero-shot-natural/)
- [Survey of Low-Resource Machine Translation](https://md.research.elamur.ai/free/en/papers/haddow-2022-survey-low-resource-machine-translation/)
- [A Survey on Recent Approaches for Natural Language Processing in Low-Resource Scenarios](https://md.research.elamur.ai/free/en/papers/hedderich-2021-survey-recent-approaches-natural-language/)
- [Quality at a Glance: An Audit of Web-Crawled Multilingual Datasets](https://md.research.elamur.ai/free/en/papers/kreutzer-2021-quality-glance-audit-web-crawled/)
- [MADLAD-400: A Multilingual And Document-Level Large Audited Dataset](https://md.research.elamur.ai/free/en/papers/kudugunta-2023-madlad-400-multilingual-document-level/)
- [The BigScience ROOTS Corpus: A 1.6TB Composite Multilingual Dataset](https://md.research.elamur.ai/free/en/papers/lauren-2023-ccon-bigscience-roots-corpus-1-6tb/)
- [Findings of the AmericasNLP 2021 Shared Task on Open Machine Translation for Indigenous Languages of the Americas](https://md.research.elamur.ai/free/en/papers/mager-2021-findings-americasnlp-2021-shared-task/)
- [The Johns Hopkins University Bible Corpus: 1600+ Tongues for Typological Exploration](https://md.research.elamur.ai/free/en/papers/mccarthy-2020-johns-hopkins-university-bible-corpus/)
- [UniMorph 3.0: Universal Morphology](https://md.research.elamur.ai/free/en/papers/mccarthy-2020-unimorph-3-0-universal-morphology/)
- [Sentiment Analysis and Language Models for Kwanyama](https://md.research.elamur.ai/free/en/papers/nakashole-2026-sentiment-analysis-language-models-kwanyama/)
- [Participatory Research for Low-resourced Machine Translation: A Case Study in African Languages](https://md.research.elamur.ai/free/en/papers/nekoto-2020-participatory-research-low-resourced-machine/)
- [Tigrinya Neural Machine Translation with Transfer Learning for Humanitarian Response](https://md.research.elamur.ai/free/en/papers/oktem-2020-tigrinya-neural-machine-translation-transfer/)
- [XCOPA: A Multilingual Dataset for Causal Commonsense Reasoning](https://md.research.elamur.ai/free/en/papers/ponti-2020-xcopa-multilingual-dataset-causal-commonsense/)
- [Samanantar: The Largest Publicly Available Parallel Corpora Collection for 11 Indic Languages](https://md.research.elamur.ai/free/en/papers/ramesh-2022-samanantar-largest-publicly-available-parallel/)
- [Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation](https://md.research.elamur.ai/free/en/papers/reimers-2020-making-monolingual-sentence-embeddings-multilingual/)
- [The Construction of a Mixe Variant Parallel Corpus](https://md.research.elamur.ai/free/en/papers/ruiz-2026-construction-mixe-variant-parallel-corpus/)
- [Bifixer and Bicleaner: two open-source tools to clean your parallel data](https://md.research.elamur.ai/free/en/papers/sanchez-2020-bifixer-bicleaner-two-open-source/)
- [FormosanMT: A Multilingual Parallel Corpus of the Formosan Language Family](https://md.research.elamur.ai/free/en/papers/scheppat-2026-formosanmt-multilingual-parallel-corpus-formosan/)
- [IndoNLU: Benchmark and Resources for Evaluating Indonesian Natural Language Understanding](https://md.research.elamur.ai/free/en/papers/wilie-2020-indonlu-benchmark-resources-evaluating-indonesian/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{agic2019jw300,
  title = {{JW300: A Wide-Coverage Parallel Corpus for Low-Resource Languages}},
  author = {Agić, Željko and Vulić, Ivan},
  booktitle = {Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics},
  year = {2019},
  doi = {10.18653/v1/p19-1310},
  url = {https://doi.org/10.18653/v1/p19-1310}
}
```

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- [↑ back to papers of 2019](https://md.research.elamur.ai/free/en/papers/2019/page/3/)

## References

39 references, 13 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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