---
title: "Learning Word Vectors for 157 Languages — Grave 2018"
description: "Most publicly available pre-trained word embeddings cover only English."
authors: ["Edouard Grave", "Piotr Bojanowski", "Prakhar Gupta", "Armand Joulin", "Tomas Mikolov"]
year: 2018
venue: "Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)"
source: "https://aclanthology.org/L18-1550/"
arxiv: "1802.06893"
licence: "CC BY-NC 4.0 (ELRA, LREC proceedings)"
licence_url: "https://creativecommons.org/licenses/by-nc/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/grave-2018-learning-word-vectors-157-languages/"
---
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# Learning Word Vectors for 157 Languages

**Authors:** [Edouard Grave](https://md.research.elamur.ai/free/en/authors/edouard-grave/), [Piotr Bojanowski](https://md.research.elamur.ai/free/en/authors/piotr-bojanowski/), [Prakhar Gupta](https://md.research.elamur.ai/free/en/authors/prakhar-gupta/), [Armand Joulin](https://md.research.elamur.ai/free/en/authors/armand-joulin/), [Tomas Mikolov](https://md.research.elamur.ai/free/en/authors/tomas-mikolov/) · **Year:** 2018 · **Venue:** Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018) · **Licence:** [CC BY-NC 4.0 (ELRA, LREC proceedings)](https://creativecommons.org/licenses/by-nc/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/L18-1550/) · [PDF](https://aclanthology.org/L18-1550.pdf) · [arXiv 1802.06893](https://arxiv.org/abs/1802.06893) · [OpenAlex](https://openalex.org/W2788784374)

**Citations:** 187 citations (openalex-snapshot · 2026-10-14) · [451](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
Most publicly available pre-trained word embeddings cover only English. Many other languages have Wikipedias too small to support good vectors. The authors address this by building fastText embeddings for 157 languages. They combine Wikipedia dumps with the much larger but noisier Common Crawl.

The crawl is split by language line by line, using a fastText-based classifier they trained. They then drop duplicate lines through hashing and tokenize each language with a suitable segmenter. They train two subword-aware models: skipgram and a CBOW variant that adds position-dependent weights. They also create new word analogy benchmarks for French, Hindi and Polish, built by adapting the English analogies of Mikolov et al.

Across analogy tests in 10 languages, position-weighted CBOW gave the largest gain. Adding crawl data barely helps resource-rich languages but sharply improves those with small Wikipedias. Hindi still lags well behind. The released vectors, datasets and language identifier provide a broad multilingual baseline resource.

## Key points
- **Data:** The authors used Wikipedia dumps from September 11, 2017 and the May 2017 Common Crawl (roughly 24 TB of text, taken from WET files).
  - Only 28 Wikipedias exceed 100M tokens, and Hindi's has about 39M.
- **Language identifier:** The fastText classifier covers 176 languages, using character n-grams of length 2–4.
  - It beats langid.py on accuracy (e.g., 94.7 vs 93.1 on TCL) and runs several times faster.
  - Only lines longer than 100 characters with confidence ≥ 0.8 were kept.
- **Deduplication:** Hash-based removal discarded 37% of the crawl text and 21% of the Wikipedia text.
- **Analogy evaluation:** The ten test languages were Chinese, Finnish, Polish, Czech, Hindi, Portuguese, Spanish, Italian, German and French.
  - Three of these datasets are new: French, Hindi and Polish.
- **Effect of crawl data:** Adding Common Crawl brought large gains for languages with small Wikipedias: +23.5 Finnish, +17.8 Chinese, +16.0 Hindi, +9.7 Polish.
  - For German, Spanish and French, accuracy stayed about the same or dropped slightly.

*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/)

## Cited by

Cited by

451 papers of our library cite it (of the 140673 whose bibliographies we read); 43 of them have a page here, 408 not yet.

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- [Evaluating Word Embeddings on Low-Resource Languages](https://md.research.elamur.ai/free/en/papers/aclbulk-2020-stringham-evaluating-word-embeddings-low-resource/)
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## Cite

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

```bibtex
@inproceedings{grave2018learning,
  title = {{Learning Word Vectors for 157 Languages}},
  author = {Grave, Edouard and Bojanowski, Piotr and Gupta, Prakhar and Joulin, Armand and Mikolov, Tomas},
  booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)},
  year = {2018},
  doi = {10.63317/2dwkd5xar3i5},
  eprint = {1802.06893},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.63317/2dwkd5xar3i5}
}
```

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23 references, 6 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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23. NoDaLiDa. Wu, F. and Weld, D. S. (2010). Open information extraction using wikipedia. In Proc. ACL. [find](https://scholar.google.com/scholar?q=NoDaLiDa%2E+Wu%2C+F%2E+and+Weld%2C+D%2E+S%2E+%282010%29%2E+Open+information+extraction+using+wikipedia%2E+In+Proc%2E+ACL%2E) [our card](https://md.research.elamur.ai/free/en/papers/wu-2010-open-information-extraction-wikipedia/)