Machine Translation Models

Machine translation is the task of translating text from one language to another. For example, from English to Spanish. Models are based on the Transformer sequence-to-sequence architecture [nlp-machine_translation4].

An example script on how to train the model can be found here: NeMo/examples/nlp/machine_translation/enc_dec_nmt.py. The default configuration file for the model can be found at: NeMo/examples/nlp/machine_translation/conf/aayn_base.yaml.

Quick Start

from nemo.collections.nlp.models import MTEncDecModel

# To get the list of pre-trained models
MTEncDecModel.list_available_models()

# Download and load the a pre-trained to translate from English to Spanish
model = MTEncDecModel.from_pretrained("nmt_en_es_transformer12x2")

# Translate a sentence or list of sentences
translations = model.translate(["Hello!"], source_lang="en", target_lang="es")

Data Format

Supervised machine translation models require parallel corpora which comprise many examples of sentences in a source language and their corresponding translation in a target language. We use parallel data formatted as seperate text files for source and target language where sentences in corresponding files are aligned like in the table below.

Parallel Coprus

train.english.txt

train.spanish.txt

Hello .

Hola .

Thank you .

Gracias .

You can now translate from English to Spanish in NeMo .

Ahora puedes traducir del inglés al español en NeMo .

It is common practice to apply data cleaning, normalization and tokenization to the data prior to training a translation model and NeMo expects already cleaned, normalized and tokenized data. The only data pre-processing NeMo does is subword tokenization with BPE [nlp-machine_translation3].

Data Cleaning, Normalization & Tokenization

We recommend applying the following steps to clean, normalize and tokenize your data. All pre-trained models released apply these data pre-processing steps.

1. Language ID filtering - This step filters out examples from your training dataset that aren’t in the correct language. For example, many datasets contain examples where source and target sentences are in the same language . You can use a pre-trained language ID classifier from fastText. You can then run our script using the lid.176.bin model downloaded from the fastText website.

python NeMo/scripts/neural_machine_translation/filter_by_language.py \
  --input-src train.en \
  --input-tgt train.es \
  --output-src train_lang_filtered.en \
  --output-tgt train_lang_filtered.es \
  --source-lang en \
  --target-lang es \
  --removed-src train_noise.en \
  --removed-tgt train_noise.de \
  --fasttext-model lid.176.bin

2. Length filtering - We filter out sentences from the data that are below a minimum length (1) or exceed a maximum length (250). We also filter out sentences where the ratio between source and target lengths exceeds 1.3 except for English <-> Chinese models. Moses is a statistical machine translation toolkit that contains many useful pre-processing scripts.

perl mosesdecoder/scripts/training/clean-corpus-n.perl -ratio 1.3 train en es train.filter 1 250

3. Data cleaning - While language ID filtering can sometimes help with filtering out noisy sentences that contain too many puncutations, it does not help in cases where the translations are potentially incorrect, disfluent or incomplete. We use bicleaner a tool to identify such sentences. It trains a classifier based on many features included pre-trained language model fluency, word alignment scores from a word-alignment model like Giza++ etc. We use their available pre-trained models wherever possible and train models ourselves using their framework for remaining languages. The following script will apply a pre-trained bicleaner model to the data and pick sentences that are clean with probability > 0.5.

awk '{print "-\t-"}' train.en \
| paste -d "\t" - train.filter.en train.filter.es \
| bicleaner-classify - - </path/to/bicleaner.yaml> > train.en-es.bicleaner.score

4. Data deduplication - We use bifixer (which uses xxHash) to hash the source and target sentences based on which we remove duplicate entries from the file. You may want to do something similar to remove training examples that are in the test dataset.

cat train.en-es.bicleaner.score \
  | parallel -j 25 --pipe -k -l 30000 python bifixer.py --ignore-segmentation -q - - en es \
  > train.en-es.bifixer.score

awk -F awk -F "\t" '!seen[$6]++' train.en-es.bifixer.score > train.en-es.bifixer.dedup.score

Filter out data that bifixer assigns probability < 0.5 to.

awk -F "\t" '{ if ($5>0.5) {print $3}}' train.en-es.bifixer.dedup.score > train.cleaned.en
awk -F "\t" '{ if ($5>0.5) {print $4}}' train.en-es.bifixer.dedup.score > train.cleaned.es

  1. Punctuation Normalization - Punctuation, especially things like quotes can be written in different ways and its often useful to normalize the way they appear in text. We use the moses punctuation normalizer on all languages except Chinese.

perl mosesdecoder/scripts/tokenizer/normalize-punctuation.perl -l es < train.cleaned.es > train.normalized.es
perl mosesdecoder/scripts/tokenizer/normalize-punctuation.perl -l en < train.cleaned.en > train.normalized.en

Example:

Before - Aquí se encuentran joyerías como Tiffany`s entre negocios tradicionales suizos como la confitería Sprüngli.
After  - Aquí se encuentran joyerías como Tiffany's entre negocios tradicionales suizos como la confitería Sprüngli.

  1. Tokenization and word segmentation for Chinese

Naturally written text often contains punctuation markers like commas, full-stops and apostrophes that are attached to words. Tokenization by just splitting a string on spaces will result in separate token IDs for very similar items like “NeMo” and “NeMo.”. Tokenization splits punctuation from the word to create two separate tokens. In the previous example “NeMo.” becomes “NeMo .” which when split by space results in two tokens and adressess the earlier problem. Example:

Before - Especialmente porque se enfrentará "a Mathieu (Debuchy), Yohan (Cabaye) y Adil (Rami) ", recuerda.
After  - Especialmente porque se enfrentará " a Mathieu ( Debuchy ) , Yohan ( Cabaye ) y Adil ( Rami ) " , recuerda .

We use the Moses tokenizer for all languages except Chinese.

perl mosesdecoder/scripts/tokenizer/tokenizer.perl -l es -no-escape < train.normalized.es > train.tokenized.es
perl mosesdecoder/scripts/tokenizer/tokenizer.perl -l en -no-escape < train.normalized.en > train.tokenized.en

For languages like Chinese where there is no explicit marker like spaces that separate words, we use Jieba to segment a string into words that are space separated. Example:

Before - 同时，卫生局认为有必要接种的其他人员，包括公共部门，卫生局将主动联络有关机构取得名单后由卫生中心安排接种。
After  - 同时 ， 卫生局 认为 有 必要 接种 的 其他 人员 ， 包括 公共部门 ， 卫生局 将 主动 联络 有关 机构 取得 名单 后 由 卫生 中心 安排 接种 。

Training a BPE Tokenization

Byte-pair encoding (BPE) [nlp-machine_translation3] is a sub-word tokenization algorithm that is commonly used to reduce the large vocabulary size of datasets by splitting words into frequently occuring sub-words. Currently, Machine Translation only supports the YouTokenToMe BPE tokenizer. One can set the tokenization configuration as follows:

Parameter

Data Type

Default

Description

model.{encoder_tokenizer,decoder_tokenizer}.tokenizer_name

str

yttm

BPE library name. Only supports yttm for now.

model.{encoder_tokenizer,decoder_tokenizer}.tokenizer_model

str

null

Path to an existing YTTM BPE model. If null, will train one from scratch on the provided data.

model.{encoder_tokenizer,decoder_tokenizer}.vocab_size

int

null

Desired vocabulary size after BPE tokenization

model.{encoder_tokenizer,decoder_tokenizer}.bpe_dropout

float

null

BPE dropout probability. [nlp-machine_translation2]

model.{encoder_tokenizer,decoder_tokenizer}.vocab_file

str

null

Path to pre-computed vocab file if exists

model.shared_tokenizer

bool

True

Whether to share the tokenizer between the encoder and decoder

Applying BPE Tokenization, batching, bucketing and padding

Given BPE tokenizers, and a cleaned parallel corpus, the following steps are applied to create a a TranslationDataset object.

  1. Text to IDs - This performs subword tokenization with the BPE model on an input string and maps it to a sequence of tokens for the source and target text.

  2. Bucketing - Sentences vary in length and when creating minibatches, we’d like sentences in them have roughly the same length to minimize the number of <pad> tokens, to maximize computational efficiency. This step groups sentences on roughly the same length into buckets.

  3. Batching and padding - Creates minibatches of with a maximum number of tokens specified by model.{train_ds,validation_ds,test_ds}.tokens_in_batch from buckets and pads the sequences to pack them so they can be packed into a tensor.

Datasets can be configured as follows

Parameter

Data Type

Default

Description

model.{train_ds,validation_ds,test_ds}.src_file_name

str

null

Path to the source language file

model.{train_ds,validation_ds,test_ds}.tgt_file_name

str

null

Path to the target language file

model.{train_ds,validation_ds,test_ds}.tokens_in_batch

int

512

Maximum number of tokens per minibatch

model.{train_ds,validation_ds,test_ds}.clean

bool

true

Whether to clean the dataset by discarding examples that are greater than max_seq_length

model.{train_ds,validation_ds,test_ds}.max_seq_length

int

512

Maximum sequence to be used with the clean argument above.

model.{train_ds,validation_ds,test_ds}.shuffle

bool

true

Whether to shuffle minibatches in the PyTorch DataLoader

model.{train_ds,validation_ds,test_ds}.num_samples

int

-1

Number of samples to use. -1 for the entire dataset

model.{train_ds,validation_ds,test_ds}.drop_last

bool

false

Drop last minibatch if it is not of equal size to the others

model.{train_ds,validation_ds,test_ds}.pin_memory

bool

false

Whether to pin memory in the PyTorch DataLoader

model.{train_ds,validation_ds,test_ds}.num_workers

int

8

Number of workers for the PyTorch DataLoader

Tarred Datasets for Large Corpora

When training with DistributedDataParallel, each process has its own copy of the dataset and for large datasets, this may not always fit in CPU memory. Webdatasets circumvents this problem by efficiently iterating over tarfiles stored on disk. Each tarfile may contain hundreds to thousands of pickle files, each containing a single minibatch.

We strongly recommend using this method when working with datasets with > 1 million sentence pairs.

Tarred datasets can be configured as follows:

Parameter

Data Type

Default

Description

model.{train_ds,validation_ds,test_ds}.use_tarred_dataset

bool

false

Whether to use tarred datasets

model.{train_ds,validation_ds,test_ds}.tar_files

str

null

String specifying path to all tarfiles. Example with 100 tarfiles /path/to/tarfiles._OP_1..100_CL_.tar

model.{train_ds,validation_ds,test_ds}.metadata_file

str

null

Path to JSON metadata file that contains only a single entry for the total number of batches in the dataset

model.{train_ds,validation_ds,test_ds}.lines_per_dataset_fragment

int

1000000

This

model.{train_ds,validation_ds,test_ds}.num_batches_per_tarfile

int

100

Maximum sequence to be used with the clean argument above.

model.{train_ds,validation_ds,test_ds}.tar_shuffle_n

int

100

Whether to cache IDs to avoid re-tokenizing data. This will be deprecated in favor of tarred datasets.

model.{train_ds,validation_ds,test_ds}.shard_strategy

str

scatter

Whether to cache IDs in each of the nodes in multi-node training.

model.preproc_out_dir

str

null

Path to folder that contains processed tarfiles or directory where new tarfiles will be written

They can be created in two ways

  1. Using the hydra config and training script.

Example:

python examples/nlp/machine_translation/enc_dec_nmt.py \
  -cn aayn_base \
  do_training=false \
  model.preproc_out_dir=/path/to/preproc_dir \
  model.train_ds.use_tarred_dataset=true \
  model.train_ds.lines_per_dataset_fragment=1000000 \
  model.train_ds.num_batches_per_tarfile=200 \
  model.train_ds.src_file_name=train.tokenized.en \
  model.train_ds.tgt_file_name=train.tokenized.es \
  model.validation_ds.src_file_name=validation.tokenized.en \
  model.validation_ds.tgt_file_name=validation.tokenized.es \
  model.encoder_tokenizer.vocab_size=32000 \
  model.decoder_tokenizer.vocab_size=32000 \
  ~model.test_ds \
  trainer.gpus=[0,1,2,3] \
  +trainer.fast_dev_run=true \
  exp_manager=null \

The above script will process the parallel tokenized text files into tarred datasets that are written to /path/to/preproc_dir. Since do_training is set to False, the above script will only create tarred datasets and then exit. If do_training is set True then one of two things happen:

  1. If no tarfiles present in model.preproc_out_dir, the script will first create those files and then commence training.

  2. If tarfiles are already present in ``model.preproc_out_dir`, the script will start training from the provided tarfiles.

  1. Using a separate script without hydra

Tarred datasets for parallel corpora can also be created with a script that doesn’t require specifying a configs via hydra and just uses python argparse.

Example:

python examples/nlp/machine_translation/create_tarred_parallel_dataset.py \
  --shared_tokenizer \
  --clean \
  --bpe_dropout 0.1 \
  --src_fname train.tokenized.en \
  --tgt_fname train.tokenized.es \
  --out_dir /path/to/preproc_dir \
  --vocab_size 32000 \
  --max_seq_length 512 \
  --min_seq_length 1 \
  --tokens_in_batch 8192 \
  --lines_per_dataset_fragment 1000000 \
  --num_batches_per_tarfile 200

You can then set model.preproc_out_dir=/path/to/preproc_dir and model.train_ds.use_tarred_dataset=true to train with this data.

Model Configuration and Training

The overall model consists of an encoder, decoder and classification head. Encoders and Decoders have the following configuration options:

Our pre-trained models are optimized with Adam, with a maximum learning of 0.0004, beta of (0.9, 0.98), an inverse square root learning rate schedule from [nlp-machine_translation4]. The model.optim section sets the optimization parameters.

The following script will create tarred datasets based on the provided parallel corpus and train a model based on the base configuration from [nlp-machine_translation4].

python examples/nlp/machine_translation/enc_dec_nmt.py \
  -cn aayn_base \
  do_training=true \
  trainer.gpus=8 \
  ~trainer.max_epochs \
  +trainer.max_steps=100000 \
  +trainer.val_check_interval=1000 \
  +exp_manager.exp_dir=/path/to/store/results \
  +exp_manager.create_checkpoint_callback=True \
  +exp_manager.checkpoint_callback_params.monitor=val_sacreBLEU \
  +exp_manager.checkpoint_callback_params.mode=max \
  +exp_manager.checkpoint_callback_params.save_top_k=5 \
  model.preproc_out_dir=/path/to/preproc_dir \
  model.train_ds.use_tarred_dataset=true \
  model.train_ds.lines_per_dataset_fragment=1000000 \
  model.train_ds.num_batches_per_tarfile=200 \
  model.train_ds.src_file_name=train.tokenized.en \
  model.train_ds.tgt_file_name=train.tokenized.es \
  model.validation_ds.src_file_name=validation.tokenized.en \
  model.validation_ds.tgt_file_name=validation.tokenized.es \
  model.encoder_tokenizer.vocab_size=32000 \
  model.decoder_tokenizer.vocab_size=32000 \
  ~model.test_ds \

The trainer keeps track of the sacreBLEU score [nlp-machine_translation1] on the provided validation set and saves checkpoints that had the top 5 (by default) sacreBLEU scores.

At the end of training, a .nemo file will be written to the result directory using which we can run inference on a test set.

Model Inference

To generate translations on a test set and compute sacrebleu scores, the inference script can be run as follow:

python examples/nlp/machine_translation/nmt_transformer_infer.py \
  --model /path/to/model.nemo \
  --srctext test.en \
  --tgtout test.en-es.translations \
  --batch_size 128 \
  --source_lang en \
  --target_lang es

The –srctext file must be provided before tokenization and normalization. The resulting –tgtout file is detokenized and can be used to compute sacreBLEU scores.

cat test.en-es.translations | sacrebleu test.es

References

nlp-machine_translation1

Matt Post. A call for clarity in reporting bleu scores. arXiv preprint arXiv:1804.08771, 2018.

nlp-machine_translation2

Ivan Provilkov, Dmitrii Emelianenko, and Elena Voita. Bpe-dropout: simple and effective subword regularization. arXiv preprint arXiv:1910.13267, 2019.

nlp-machine_translation3(1,2)

Rico Sennrich, Barry Haddow, and Alexandra Birch. Neural machine translation of rare words with subword units. arXiv preprint arXiv:1508.07909, 2015.

nlp-machine_translation4(1,2,3)

Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. arXiv preprint arXiv:1706.03762, 2017.