Mohamed Anwar

I'm an ex-AI research resident at Meta

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2 mins read

Published by: Google Research

mBERT: Multilingual BERT

mBERT is a multilingual BERT pre-trained on 104 languages, released by the authors of the original paper on Google Research’s official GitHub repository: google-research/bert on November 2018. mBERT follows the same structure of BERT. The only difference is that mBERT is pre-trained on concatenated Wikipedia data for 104 languages and it does surprisingly well compared to cross-lingual word embeddings on zero-shot cross-lingual transfer in XNLI dataset.

XNLI results are promising, but the question is: does mBERT learn a cross-lingual space that supports zero-shot transfer? This paper: Beto, Bentz, Becas: The Surprising Cross-Lingual Effectiveness of BERT published in 2019 by John Hopkins University explores the cross-lingual potential of mBERT on five different NLP tasks: NLI, NER, POS tagging, MLDoc classification, and dependency parsing. According to the following graph, all five tasks cover 39 languages out of the 104 languages that mBERT was pre-trained on. The official code of this paper can be found on this GitHub repository: crosslingual-nlp.

Note:
I suggest reading the BERT part before going on to the next part.

Results

All the following results were obtained by using the mBERT~BASE~ which $12$ transformer blocks, each has $12$ attention heads and $d_{h} = 768$ hidden dimensions forming around $179M$ parameters. mBERT uses dropout of $0.1$ as a regularizer.

For each task, no preprocessing is performed except tokenization of words into subwords with WordPiece. The vocabulary size for all 104 languages about $120k$ vocabulary. For fine-tuning, they used Adam for fine-tuning with $\beta_{1} = 0.9,\ \beta_{2} = 0.999$ and L2 weight decay of $0.01$. They warmed up the learning rate over the first $10\%$ of batches using linearly decay. Best hyper-parameters was selected by searching a combination of batch size, learning rate and the number of fine-tuning epochs with the following range:

  • Learning rate: $\left\{ 2 \times 10^{- 5},\ 3 \times 10^{- 5},\ 5 \times 10^{- 5} \right\}$.

  • Batch size: $\left\{ 16,\ 32 \right\}$.

  • Number of epochs: $\left\{ 3,\ 4 \right\}$.

In the following results, ♠ denotes the mBERT version trained on bitext data found in this paper: Massively multilingual sentence embeddings for zeroshot cross-lingual transfer and beyond while † denoted to XLM model proposed by this paper: Cross-lingual Language Model Pretraining. All results were obtained by the model selected by development performance in English. However, ◊ denotes model selection with target language dev set instead. The best results are going to be bold-faced, while the second best results are going to be underlined according to the following NLP tasks:

  • MLDOC classification: On average, mBERT is the second best model after the XLM model with a very small margin.
  • XNLI: On average, mBERT performs relatively worse than XLM.
  • NER: On average, mBERT outperforms other models on this task.
  • POS Tagging:
  • Dependency Parsing: