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Models

There are two multilingual models currently available. We do not plan to release more single-language models, but we may release BERT-Large versions of these two in the future:

The Multilingual Cased (New) model also fixes normalization issues in many languages, so it is recommended in languages with non-Latin alphabets (and is often better for most languages with Latin alphabets). When using this model, make sure to pass --do_lower_case=false to run_pretraining.py and other scripts.

See the list of languages that the Multilingual model supports. The Multilingual model does include Chinese (and English), but if your fine-tuning data is Chinese-only, then the Chinese model will likely produce better results.

Results

To evaluate these systems, we use the XNLI dataset dataset, which is a version of MultiNLI where the dev and test sets have been translated (by humans) into 15 languages. Note that the training set was machine translated (we used the translations provided by XNLI, not Google NMT). For clarity, we only report on 6 languages below:

System English Chinese Spanish German Arabic Urdu
XNLI Baseline - Translate Train 73.7 67.0 68.8 66.5 65.8 56.6
XNLI Baseline - Translate Test 73.7 68.3 70.7 68.7 66.8 59.3
BERT - Translate Train Cased 81.9 76.6 77.8 75.9 70.7 61.6
BERT - Translate Train Uncased 81.4 74.2 77.3 75.2 70.5 61.7
BERT - Translate Test Uncased 81.4 70.1 74.9 74.4 70.4 62.1
BERT - Zero Shot Uncased 81.4 63.8 74.3 70.5 62.1 58.3

The first two rows are baselines from the XNLI paper and the last three rows are our results with BERT.

Translate Train means that the MultiNLI training set was machine translated from English into the foreign language. So training and evaluation were both done in the foreign language. Unfortunately, training was done on machine-translated data, so it is impossible to quantify how much of the lower accuracy (compared to English) is due to the quality of the machine translation vs. the quality of the pre-trained model.

Translate Test means that the XNLI test set was machine translated from the foreign language into English. So training and evaluation were both done on English. However, test evaluation was done on machine-translated English, so the accuracy depends on the quality of the machine translation system.

Zero Shot means that the Multilingual BERT system was fine-tuned on English MultiNLI, and then evaluated on the foreign language XNLI test. In this case, machine translation was not involved at all in either the pre-training or fine-tuning.

Note that the English result is worse than the 84.2 MultiNLI baseline because this training used Multilingual BERT rather than English-only BERT. This implies that for high-resource languages, the Multilingual model is somewhat worse than a single-language model. However, it is not feasible for us to train and maintain dozens of single-language model. Therefore, if your goal is to maximize performance with a language other than English or Chinese, you might find it beneficial to run pre-training for additional steps starting from our Multilingual model on data from your language of interest.

Here is a comparison of training Chinese models with the Multilingual BERT-Base and Chinese-only BERT-Base:

System Chinese
XNLI Baseline 67.0
BERT Multilingual Model 74.2
BERT Chinese-only Model 77.2

Similar to English, the single-language model does 3% better than the Multilingual model.

Fine-tuning Example

The multilingual model does not require any special consideration or API changes. We did update the implementation of BasicTokenizer in tokenization.py to support Chinese character tokenization, so please update if you forked it. However, we did not change the tokenization API.

To test the new models, we did modify run_classifier.py to add support for the XNLI dataset. This is a 15-language version of MultiNLI where the dev/test sets have been human-translated, and the training set has been machine-translated.

To run the fine-tuning code, please download the XNLI dev/test set and the XNLI machine-translated training set and then unpack both .zip files into some directory $XNLI_DIR.

To run fine-tuning on XNLI. The language is hard-coded into run_classifier.py (Chinese by default), so please modify XnliProcessor if you want to run on another language.

This is a large dataset, so this will training will take a few hours on a GPU (or about 30 minutes on a Cloud TPU). To run an experiment quickly for debugging, just set num_train_epochs to a small value like 0.1.

export BERT_BASE_DIR=/path/to/bert/chinese_L-12_H-768_A-12 # or multilingual_L-12_H-768_A-12
export XNLI_DIR=/path/to/xnli

python run_classifier.py \
  --task_name=XNLI \
  --do_train=true \
  --do_eval=true \
  --data_dir=$XNLI_DIR \
  --vocab_file=$BERT_BASE_DIR/vocab.txt \
  --bert_config_file=$BERT_BASE_DIR/bert_config.json \
  --init_checkpoint=$BERT_BASE_DIR/bert_model.ckpt \
  --max_seq_length=128 \
  --train_batch_size=32 \
  --learning_rate=5e-5 \
  --num_train_epochs=2.0 \
  --output_dir=/tmp/xnli_output/

With the Chinese-only model, the results should look something like this:

 ***** Eval results *****
eval_accuracy = 0.774116
eval_loss = 0.83554
global_step = 24543
loss = 0.74603

Details

Data Source and Sampling

The languages chosen were the top 100 languages with the largest Wikipedias. The entire Wikipedia dump for each language (excluding user and talk pages) was taken as the training data for each language

However, the size of the Wikipedia for a given language varies greatly, and therefore low-resource languages may be "under-represented" in terms of the neural network model (under the assumption that languages are "competing" for limited model capacity to some extent).

However, the size of a Wikipedia also correlates with the number of speakers of a language, and we also don't want to overfit the model by performing thousands of epochs over a tiny Wikipedia for a particular language.

To balance these two factors, we performed exponentially smoothed weighting of the data during pre-training data creation (and WordPiece vocab creation). In other words, let's say that the probability of a language is P(L), e.g., P(English) = 0.21 means that after concatenating all of the Wikipedias together, 21% of our data is English. We exponentiate each probability by some factor S and then re-normalize, and sample from that distribution. In our case we use S=0.7. So, high-resource languages like English will be under-sampled, and low-resource languages like Icelandic will be over-sampled. E.g., in the original distribution English would be sampled 1000x more than Icelandic, but after smoothing it's only sampled 100x more.

Tokenization

For tokenization, we use a 110k shared WordPiece vocabulary. The word counts are weighted the same way as the data, so low-resource languages are upweighted by some factor. We intentionally do not use any marker to denote the input language (so that zero-shot training can work).

Because Chinese (and Japanese Kanji and Korean Hanja) does not have whitespace characters, we add spaces around every character in the CJK Unicode range before applying WordPiece. This means that Chinese is effectively character-tokenized. Note that the CJK Unicode block only includes Chinese-origin characters and does not include Hangul Korean or Katakana/Hiragana Japanese, which are tokenized with whitespace+WordPiece like all other languages.

For all other languages, we apply the same recipe as English: (a) lower casing+accent removal, (b) punctuation splitting, (c) whitespace tokenization. We understand that accent markers have substantial meaning in some languages, but felt that the benefits of reducing the effective vocabulary make up for this. Generally the strong contextual models of BERT should make up for any ambiguity introduced by stripping accent markers.

List of Languages

The multilingual model supports the following languages. These languages were chosen because they are the top 100 languages with the largest Wikipedias:

  • Afrikaans
  • Albanian
  • Arabic
  • Aragonese
  • Armenian
  • Asturian
  • Azerbaijani
  • Bashkir
  • Basque
  • Bavarian
  • Belarusian
  • Bengali
  • Bishnupriya Manipuri
  • Bosnian
  • Breton
  • Bulgarian
  • Burmese
  • Catalan
  • Cebuano
  • Chechen
  • Chinese (Simplified)
  • Chinese (Traditional)
  • Chuvash
  • Croatian
  • Czech
  • Danish
  • Dutch
  • English
  • Estonian
  • Finnish
  • French
  • Galician
  • Georgian
  • German
  • Greek
  • Gujarati
  • Haitian
  • Hebrew
  • Hindi
  • Hungarian
  • Icelandic
  • Ido
  • Indonesian
  • Irish
  • Italian
  • Japanese
  • Javanese
  • Kannada
  • Kazakh
  • Kirghiz
  • Korean
  • Latin
  • Latvian
  • Lithuanian
  • Lombard
  • Low Saxon
  • Luxembourgish
  • Macedonian
  • Malagasy
  • Malay
  • Malayalam
  • Marathi
  • Minangkabau
  • Nepali
  • Newar
  • Norwegian (Bokmal)
  • Norwegian (Nynorsk)
  • Occitan
  • Persian (Farsi)
  • Piedmontese
  • Polish
  • Portuguese
  • Punjabi
  • Romanian
  • Russian
  • Scots
  • Serbian
  • Serbo-Croatian
  • Sicilian
  • Slovak
  • Slovenian
  • South Azerbaijani
  • Spanish
  • Sundanese
  • Swahili
  • Swedish
  • Tagalog
  • Tajik
  • Tamil
  • Tatar
  • Telugu
  • Turkish
  • Ukrainian
  • Urdu
  • Uzbek
  • Vietnamese
  • Volapük
  • Waray-Waray
  • Welsh
  • West Frisian
  • Western Punjabi
  • Yoruba

The Multilingual Cased (New) release contains additionally Thai and Mongolian, which were not included in the original release.