This repository contains source code for the paper "Language Model Prior for Low-Resource Neural Machine Translation" (Paper)

Introduction

In this work, we use a language model (LM) trained on target-side monolingual corpora as a weakly informative prior. We add a regularization term, which drives the output distributions of the translation model (TM) to be probable under the distributions of the LM.

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UPDATE: You can now use the LM-prior with fairseq using the plug-in under fairseq_extension.

Prerequisites

Install Requirements

Create Environment (Optional): Ideally, you should create an environment for the project.

conda create -n lmprior python=3
conda activate lmprior

Install PyTorch 1.4 (guide) with the desired Cuda version if you want to use the GPU:

# CUDA 10.1
pip install torch==1.4.0 torchvision==0.5.0

and then the rest of the requirements:

pip install -r requirements.txt

Download Data

1. Parallel data: You can download the preprocessed data, the truecase models and the pretrained sentencepiece models from this link: http://data.statmt.org/cbaziotis/projects/lm-prior/parallel. Put the wmt_ende and wmt_entr folders in the datasets/mt/ directory.

To prepare the data on your own:

1. run datasets/mt/download_data.sh
2. run datasets/mt/preprocess_parallel.sh

2. Monolingual data: You can download the preprocessed data from this link: http://data.statmt.org/cbaziotis/projects/lm-prior/mono and then put the files in the datasets/mono/priors/ directory.

Training

Run Visdom server (required)

We use Visdom for visualizing the training progress. Therefore, first open a terminal and run the visdom server:

> visdom

Once you start training a model, open the visdom dashboard in your browser (by default in http://localhost:8097/) and select a model to view its statistics, or multiple models to compare them.

Read more about visdom here: https://github.com/facebookresearch/visdom#usage

How to train a model

Every model requires a base configuration stored in a .yaml file. All model configurations are stored in the configs/ directory. When you run an experiment you need to provide a base config and optionally override the parameters in the config file.

For example, you can train a LM on a small test corpus like this:

/models$  python sent_lm.py --config ../configs/prototype.rnn_lm_en.yaml

To override one of the parameters in the config, you don't have to create a new one, just pass the parameter-value pair like that:

/models$  python sent_lm.py --config ../configs/prototype.rnn_lm_en.yaml  model.emb_size=256

For nested parameters, separate the names with ..

Experiment output: For every model that is trained, all its data, including the checkpoint, outputs and its training progress, are saved in the experiments/ directory, under experiments/CONFIG_NAME/START_DATETIME. For instance, a model trained with the command above will be saved under: experiments/prototype.rnn_lm_en/20-10-21_16:03:22.

Verify that the model is training by opening visdom and selecting the model from the search bar.

1. Train a language model (LM)

To train an LM you need to run models/sent_lm.py using the desired config. For example, to train an English Transformer-based LM on the 3M NewsCrawl data, same as in the paper, use the config configs/transformer/prior.lm_news_en_trans.yaml and (optionally) pass any parameters to override those in the config file:

/models$ python sent_lm.py --config ../configs/transformer/prior.lm_news_en_trans.yaml  \
  --device cuda  --name prior.lm_news_en_3M_trans_big \ 
  batch_tokens=12000 model.emb_size=1024 model.nhid=4096 model.nhead=16 model.dropout=0.3

If you open configs/transformer/prior.lm_news_en_trans.yaml you will see that it expects a preprocessed dataset:

  ...

   data:
     train_path: ../datasets/mono/priors/news.en.2014-2017.pp.3M.train
     val_path:   ../datasets/mono/priors/news.en.2014-2017.pp.val
     subword_path: ../datasets/mt/wmt_ende/en.16000

  ...

You can change the path to your own preprocessed dataset or download t he prepared data we used in the paper.

Reproducibility: You can find the exact commands that were used for training the LMs used in the paper in configs/transformer/experiments_priors.sh.

Sanity check Verify that the model is training correctly by looking at the loss and model outputs (samples) in visdom. You can test that everything is working correctly by trying first with a small model. You should start to see reasonable sentences after a while.

2. Train a translation model (TM)

To train a TM you need to run models/nmt_prior.py using the desired config. For the Transformer-based experiments, check the config files in configs/transformer/.

Train a standard TM

To train a standard TM for de->en with a transformer architecture run:

/models$  nmt_prior.py --config ../../configs/transformer/trans.deen_base.yaml --name final.trans.deen_base

All the model outputs will be saved in experiments/trans.deen_base/START_DATETIME/, including the checkpoint of the model that has achieved the best score in the dev set.

Train a TM with a LM-prior

To train a standard TM for de->en with a transformer architecture run:

/models$  nmt_prior.py --config ../../configs/transformer/trans.deen_prior.yaml --name final.trans.deen_base

If you open configs/transformer/trans.deen_prior.yaml you will see that it expects a pretrained LM

  ...

  # path to pretrained LM. It is required for LM-Fusion and LM-priors
  prior_path: ../checkpoints/prior.lm_news_en_3M_trans_best.pt

  ...

You can change the path to your pretrained LM or download on the pretrained LMs we used in the paper from: http://data.statmt.org/cbaziotis/projects/lm-prior/checkpoints/.

Reproducibility

In the following files, you will find all the commands for reproducing the experiments in the paper:

• configs/transformer/experiments_nmt.sh contains the commands for the main NMT experiments.
• configs/transformer/experiments_nmt_subsample_deen.sh contains the commands for the NMT experiments on various scales of the en->de parallel data.
• configs/transformer/experiments_sensitivity.sh contains the commands for the sensitivity analysis.

Evaluation

To evaluate a pretrained translation model you need to use models/translate.py.

$ python models/translate.py --help

usage: translate.py [-h] [--src SRC] [--out OUT] [--cp CP] [--ref REF]
                    [--beam_size BEAM_SIZE] [--length_penalty LENGTH_PENALTY]
                    [--lm LM] [--fusion FUSION] [--fusion_a FUSION_A]
                    [--batch_tokens BATCH_TOKENS] [--device DEVICE]

optional arguments:
  -h, --help            show this help message and exit
  --src SRC             Preprocessed input file, in source language.
  --out OUT             The name of the *detokenized* output file, in the
                        target language.
  --cp CP               The checkpoint of the translation model.
  
  --ref REF             (optional) The raw reference file, 
                        to internally compute BLEU by calling sacreBLEU.
                        
  --beam_size BEAM_SIZE 
                        The width of the beam search (default=1)
  
  --length_penalty LENGTH_PENALTY
                        The value of the length penalty (default=1.0)
                        
  --lm LM               The checkpoint of a pretrained language
                        model.Applicable when using LM fusion.
  --fusion FUSION       The type of LM-fusion to use. 
                        Options: [shallow, postnorm, prenorm]
  --fusion_a FUSION_A   This is the weight for the LM in shallow-fusion.
  --batch_tokens BATCH_TOKENS
                        The size of the batch in number of tokens.
  --device DEVICE       The devide id to use (e.g., cuda, cuda:2, cpu, ...)

Important: When using POSTNORM, in test time you should use the same LM checkpoint that you used during training the TM.

To evaluate a pretrained translation model, run:

# translate the preprocessed input file (DE)
python models/translate.py --src datasets/mt/wmt_ende/test.de.pp \ 
  --out test.en.pp.hyps \
  --cp experiments/trans.deen_base/START_DATETIME/trans.deen_base_best.pt \ 
  --beam_size 5 --device cuda

# compare the raw detokenized hypothesis file (EN'), with the raw test set (EN)
cat test.en.pp.hyps | sacrebleu datasets/mt/wmt_ende/test.en
> BLEU+case.mixed+numrefs.1+smooth.exp+tok.13a+version.1.4.14 = 25.4 59.8/32.9/20.1/12.6 (BP = 0.954 ratio = 0.955 hyp_len = 64022 ref_len = 67012)

This is the same way you evaluate a TM that was trained with a LM-prior, as the LM is not needed in test time.

To evaluate a pretrained translation model with shallow-fusion and with a weight of λ=0.1, run:

# translate the preprocessed input file (DE)
python models/translate.py --src datasets/mt/wmt_ende/test.de.pp \ 
  --out test.en.pp.hyps \
  --cp experiments/trans.deen_base/START_DATETIME/trans.deen_base_best.pt \ 
  --lm checkpoints/prior.lm_en.pt  --fusion shallow  --fusion_a 0.1 \
  --beam_size 5 --device cuda

# compare the raw detokenized hypothesis file (EN'), with the raw test set (EN)
$ cat test.en.pp.hyps | sacrebleu datasets/mt/wmt_ende/test.en
BLEU+case.mixed+numrefs.1+smooth.exp+tok.13a+version.1.4.14 = 26.1 60.0/33.4/20.7/13.2 (BP = 0.962 ratio = 0.962 hyp_len = 64484 ref_len = 67012)

Analysis

To view more information about the analysis done in the paper go to: http://data.statmt.org/cbaziotis/projects/lm-prior/analysis

Reference

@inproceedings{baziotis-etal-2020-language,
    title = "Language Model Prior for Low-Resource Neural Machine Translation",
    author = "Baziotis, Christos  and
      Haddow, Barry  and
      Birch, Alexandra",
    booktitle = "Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.615",
    doi = "10.18653/v1/2020.emnlp-main.615",
    pages = "7622--7634"
}