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WikiMatrix: Mining 135M Parallel Sentences in 1620 Language Pairs from Wikipedia

The goal of this project is to mine for parallel sentences in the textual content of Wikipedia for all possible language pairs.

Mined data

wget https://dl.fbaipublicfiles.com/laser/WikiMatrix/v1/WikiMatrix.en-fr.tsv.gz

Replace "en-fr" with the ISO codes of the desired language pair. The language pair must be in alphabetical order, e.g. "de-en" and not "en-de". The list of available bitexts and their sizes are given in the file list_of_bitexts.txt. Please do not loop over all files since AWs implements some limitations to avoid abuse.

Use this command if you want to download all 1620 language pairs in one tar file (but this is 65GB!):

wget https://dl.fbaipublicfiles.com/laser/WikiMatrix/WikiMatrix.v1.1620_language_pairs.tar

Approach

We use LASER's bitext mining approach and encoder for 93 languages [2,3]. We do not use the inter-language links provided by Wikipedia, but search over all Wikipedia articles of each language. We approach the computational challenge to mine in almost 600 million sentences by using fast indexing and similarity search with FAISS. Prior to mining parallel sentences, we perform sentence segmentation, deduplication and language identification. Please see reference [1] for details.

Data extraction and threshold optimization

We provide a tool to extract parallel texts from the the TSV files:

python3 extract.py \
  --tsv WikiMatrix.en-fr.tsv.gz \
  --bitext WikiMatrix.en-fr.txt \
  --src-lang en --trg-lang fr \
  --threshold 1.04

One can specify the threshold on the margin score. The higher the value, the more likely the sentences are mutual translations, but the less data one will get. A value of 1.04 seems to be good choice for most language pairs. Please see the analysis in the paper for more information [1].

Evaluation

To assess the quality of the mined bitexts, we trained neural MT system on all language pairs for which we were able to mine at least 25k parallel sentences (with a margin threshold of 1.04). We trained systems in both directions, source to target and target to source, and report BLEU scores on the TED test set proposed in [4]. This totals 1886 different NMT systems. This table shows the BLEU scores for the most frequest language pairs. We achieve BLEU scores over 30 for several language pairs.

The goal is not to build state of the art systems for each language pair, but to get an indication of the quality of the automatically mined data. These BLEU scores should be of course appreciated in context of the sizes of the mined corpora.

Obviously, we can not exclude that the provided data contains some wrong alignments even though the margin is large. Finally, we would like to point out that we run our approach on all available languages in Wikipedia, independently of the quality of LASER's sentence embeddings for each one.

License

The mined data is distributed under the Creative Commons Attribution-ShareAlike license.

Please cite reference [1] if you use this data.

References

[1] Holger Schwenk, Vishrav Chaudhary, Shuo Sun, Hongyu Gong and Paco Guzman, WikiMatrix: Mining 135M Parallel Sentences in 1620 Language Pairs from Wikipedia arXiv, July 11 2019.

[2] Mikel Artetxe and Holger Schwenk, Margin-based Parallel Corpus Mining with Multilingual Sentence Embeddings arXiv, Nov 3 2018.

[3] Mikel Artetxe and Holger Schwenk, Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond arXiv, Dec 26 2018.

[4] Ye Qi, Devendra Sachan, Matthieu Felix, Sarguna Padmanabhan and Graham Neubig, When and Why Are Pre-Trained Word Embeddings Useful for Neural Machine Translation? NAACL, pages 529-535, 2018.