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README.md

 
 

many-to-many-full-results-per-language.md

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README.md

Improving Massively Multilingual Neural Machine Translation and Zero-Shot Translation

We study massively multilingual translation in this paper, with a goal of improving zero-shot translation.

  • On massively multilingual translation, we collected OPUS-100, a 100-language multilingual dataset from OPUS. We release this dataset to help further study on this direction.

  • Multilingual model requires stronger capacity to support various language pairs. We propose to devise language-specific components: 1. language-aware layer normalization and 2. language-aware linear mapping and deepen the NMT architecture: deep Transformer.

    • Improving capacity clearly improves multilingual translation, and benefits specifically low-resource translation.

  • On zero-shot translation, we observe that traditional multilingual model often suffer from off-target translation [1,2].

    The examples below are for French->German translation, while multilingual models are trained on English<->French and English<->German dataset:
    Source Jusqu'à ce qu'on trouve le moment clé, celui où tu as su que tu l'aimais.
    Reference Bis wir den unverkennbaren Moment gefunden haben, den Moment, wo du wusstest, du liebst ihn.
    Zero-Shot Jusqu'à ce qu'on trouve le moment clé, celui où tu as su que tu l'aimais.
    ----------- ----------------------------------------------------------------------------------------------
    Source Les États membres ont été consultés et ont approuvé cette proposition.
    Reference Die Mitgliedstaaten wurden konsultiert und sprachen sich für diesen Vorschlag aus.
    Zero-Shot Les Member States have been consultedés and have approved this proposal.

    Multilingual model tends to copy the source input or translate into English on zero-shot directions. To support enormous zero-shot directions (~10000), we introduce random online backtranslation which operates backtranslation by randomly pick an intermediate language.

    • Stronger capacity fails to solve the off-target translation issue.
    • Random online backtranslation works and scales very well to massively zero-shot translations.

Pretrained Multilingual Models (many-to-many)

Training a multilingual model is time-consuming. We spent several weeks for one model with 4 P100 GPUs. We offer our pretrained models below to ease the study of massively multilingual translation:

Model Description Download
Base + 6 layer download
Base + 6 layer + RoBT download
Ours + 6 layer download
Ours + 6 layer + RoBT download
Ours + 12 layer download
Ours + 12 layer + RoBT download
Ours + 24 layer download
Ours + 24 layer + RoBT download

  • Base: transformer. Notice that to use these base models, you need the master version of zero rather than the multilingual one.

  • Ours: transformer + merged attention + LaLn + LaLT

  • Some different preprocessing used in our experiments (not suggested!)

    • We adopted "--character_coverage 0.9995 --input_sentence_size=10000000" for sentencepiece model training. This coverage rate results in messy code for some languages like Chinese.
    • We cut the length of test sentence to 100 during evaluation to avoid memory issue.

  • The sentencepiece model and vocabulary used in our experiments are given here. We also provide an example evaluation script.

Going-Through Example

To ease others quickly using the pretrained models and our source code, below we show a simple script. Note it's used to decoding the opus test set with the above pretrained models.

#! /bin/bash

# below is an example showing how to use our pretrained models to perform evaluation


# download the zero-toolkit 
git clone --branch multilingual_laln_lalt https://github.com/bzhangGo/zero.git

# download opus-100 to opus-100-corpus
# slow
# bash zero/scripts/data/download_opus100.sh zero/ opus-100-corpus
# faster, note this downloading might not be stable, if you cannot download the data, try multiple times or other alternative methods (like the above
 slow one)
wget https://object.pouta.csc.fi/OPUS-100/v1.0/opus-100-corpus-v1.0.tar.gz
tar xfvz opus-100-corpus-v1.0.tar.gz

# adjust document structure (to meet requirement of example_evaluation.sh)
if [[ ! -d opus-100-corpus ]]; then
    echo 'please downloading the opus-100-corpus'
    exit 1
fi

cd opus-100-corpus/
ln -s v1.0/* .
cd ..

# download our preprocessed subword models
wget http://data.statmt.org/bzhang/acl2020_multilingual/submodels.tar.gz
tar xfvz submodels.tar.gz

# download Ours-L6-RoBT model for example
wget http://data.statmt.org/bzhang/acl2020_multilingual/Ours-L6-RoBT.tar.gz 
tar xfvz Ours-L6-RoBT.tar.gz

# download the evaluation script
wget http://data.statmt.org/bzhang/acl2020_multilingual/example_evaluation.sh

# install sacrebleu, sentencepiece if necessary
pip3 install sacrebleu sentencepiece --user
# notice that we use tensorflow, so install tensorflow if necessary
# pip install tensorflow_gpu==1.13.1 --user


# perform decoding
bash example_evaluation.sh

Code

We implement models in our paper in a new branch of zero: multilingual_laln_lalt.

Please go to the new branch for code viewing:

random online backtranslation => main.py
language-aware modeling => models/transformer_multilingual

Data preprocessing

We provide a simple script to download and preprocess the OPUS-100

  • Step 1: download zero code git clone --branch multilingual_laln_lalt https://github.com/bzhangGo/zero.git

    • set code path zero_path=path-to-the-zero-code/zero, such as zero_path=`pwd`/zero

  • Step 2: download opus dataset bash $zero_path/scripts/data/download_opus100.sh $zero_path opus-100

    • Notice, speed is slow!
    • set data path opus_path=path-to-opus-100/opus-100, such as opus_path=`pwd`/opus-100

    Or instead, using wget: wget -r -np -nH -R "index.html*" -e robots=off http://data.statmt.org/opus-100-corpus/v1.0/ and set data path opus_path=`pwd`/opus-100-corpus/v1.0

  • Step 3: preprocess and prepare for one-to-many translation and many-to-many translation bash $zero_path/scripts/data/prepare_multilingual_translation.sh $opus_path $zero_path yes preprocessed_data

    • Requirement
      • python3, with sentencepiece installed
      • python2
    • !! Very very slow! (mainly due to subword processing part)
    • set preprocessed data path data_path=path-to-preprocessed-opus-100/preprocessed_data, such as data_path=`pwd`/preprocessed_data
    • Two corpus are automatically generated: $data_path/one-to-many and $data_path/many-to-many
    • Note that we employ a checkpoint file to trace the preprocessing, which might not be robust enough for handling all exceptional cases. In case you fail, consider to delete this file before reruning the script.
    • By default, we should use all development dataset for model selection. However, OPUS-100 in multilingual setting contains ~370k sentences for decoding, which could consume too many time. Instead, we offer another option to only include top-n sentences in each devset as follows (n is set to 100): bash $zero_path/scripts/data/prepare_multilingual_translation.sh $opus_path $zero_path yes preprocessed_data 100

Training

We provide an example to show how to train the model, as given in example_training.sh.

  • remember to set the data_path in example_training.py to $data_path/one-to-many for one-to-many translation or $data_path/many-to-many for many-to-many translation.

Finetuning

For many-to-many translation, we propose random online backtranslation to enable thousands of zero-shot translation directions. We show how to perform finetuning in example_finetuning.sh.

Evaluation

Multilingual evaluation in OPUS-100 include xx-en, en-xx (only for one-to-many translation), and xx-xx. We show how to do evaluation in example_evaluation.sh.

We employ sacrebleu for all evaluation.

More Results

We format our paper by only reporting average BLEU scores. For those interested in concrete results for each language pair, we provide more detailed results for many-to-many translation. We also provide an excel document for downloading.

Citation

Please consider cite our paper as follows:

Biao Zhang; Philip Williams; Ivan Titov; Rico Sennrich (2020). Improving Massively Multilingual Neural Machine Translation and Zero-Shot Translation. In Proceedings of the 2020 Annual Conference of the Association for Computational Linguistics.

@inproceedings{zhang2020,
title = "Improving Massively Multilingual Neural Machine Translation and Zero-Shot Translation",
author = "Zhang, Biao and
Williams, Philip and
Titov, Ivan and
Sennrich, Rico",
booktitle = "Proceedings of the 2020 Annual Conference of the Association for Computational Linguistics",
month = jul,
year = "2020",
publisher = "Association for Computational Linguistics"
}

References

[1] Naveen Arivazhagan, Ankur Bapna, Orhan Firat, RoeeAharoni, Melvin Johnson, and Wolfgang Macherey.2019a. The missing ingredient in zero-shot neuralmachine translation.CoRR, abs/1903.07091.

[2] Jiatao Gu, Yong Wang, Kyunghyun Cho, and Vic-tor O.K. Li. 2019. Improved zero-shot neural ma-chine translation via ignoring spurious correlations.InProceedings of the 57th Annual Meeting of theAssociation for Computational Linguistics, pages1258–1268, Florence, Italy. Association for Compu-tational Linguistics.