Cross-Lingual Language Model Pre-training

Below are some details for training Cross-Lingual Language Models (XLM) - similar to the ones presented in Lample & Conneau, 2019 - in Fairseq. The current implementation only supports the Masked Language Model (MLM) from the paper above.

Downloading and Tokenizing Monolingual Data

Pointers to the monolingual data from wikipedia, used for training the XLM-style MLM model as well as details on processing (tokenization and BPE) it can be found in the XLM Github Repository.

Let's assume the following for the code snippets in later sections to work

Processed data is in the folder: monolingual_data/processed
Each language has 3 files for train, test and validation. For example we have the following files for English: train.en, valid.en
We are training a model for 5 languages: Arabic (ar), German (de), English (en), Hindi (hi) and French (fr)
The vocabulary file is monolingual_data/processed/vocab_mlm

Fairseq Pre-processing and Binarization

Pre-process and binarize the data with the MaskedLMDictionary and cross_lingual_lm task

bash

# Ensure the output directory exists
DATA_DIR=monolingual_data/fairseq_processed
mkdir -p "$DATA_DIR"

for lg in ar de en hi fr
do

  fairseq-preprocess \
  --task cross_lingual_lm \
  --srcdict monolingual_data/processed/vocab_mlm \
  --only-source \
  --trainpref monolingual_data/processed/train \
  --validpref monolingual_data/processed/valid \
  --testpref monolingual_data/processed/test \
  --destdir monolingual_data/fairseq_processed \
  --workers 20 \
  --source-lang $lg

  # Since we only have a source language, the output file has a None for the
  # target language. Remove this

  for stage in train test valid

    sudo mv "$DATA_DIR/$stage.$lg-None.$lg.bin" "$stage.$lg.bin"
    sudo mv "$DATA_DIR/$stage.$lg-None.$lg.idx" "$stage.$lg.idx"

  done

done

Train a Cross-lingual Language Model similar to the XLM MLM model

Use the following command to train the model on 5 languages.

fairseq-train \
--task cross_lingual_lm monolingual_data/fairseq_processed \
--save-dir checkpoints/mlm \
--max-update 2400000 --save-interval 1 --no-epoch-checkpoints \
--arch xlm_base \
--optimizer adam --lr-scheduler reduce_lr_on_plateau \
--lr-shrink 0.5 --lr 0.0001 --stop-min-lr 1e-09 \
--dropout 0.1 \
--criterion legacy_masked_lm_loss \
--max-tokens 2048 --tokens-per-sample 256 --attention-dropout 0.1 \
--dataset-impl lazy --seed 0 \
--masked-lm-only \
--monolingual-langs 'ar,de,en,hi,fr' --num-segment 5 \
--ddp-backend=no_c10d

Some Notes:

Using tokens_per_sample greater than 256 can cause OOM (out-of-memory) issues. Usually since MLM packs in streams of text, this parameter doesn't need much tuning.
The Evaluation workflow for computing MLM Perplexity on test data is in progress.
Finetuning this model on a downstream task is something which is not currently available.