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- move evaluation results from 'firefox-translations-evaluation' repo - run evaluation for PRs inside this repo - automatically commit evaluation results
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# Byte-compiled / optimized / DLL files | ||
__pycache__/ | ||
*.py[cod] | ||
*$py.class | ||
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# C extensions | ||
*.so | ||
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# Distribution / packaging | ||
.Python | ||
build/ | ||
develop-eggs/ | ||
dist/ | ||
downloads/ | ||
eggs/ | ||
.eggs/ | ||
lib/ | ||
lib64/ | ||
parts/ | ||
sdist/ | ||
var/ | ||
wheels/ | ||
pip-wheel-metadata/ | ||
share/python-wheels/ | ||
*.egg-info/ | ||
.installed.cfg | ||
*.egg | ||
MANIFEST | ||
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# PyInstaller | ||
# Usually these files are written by a python script from a template | ||
# before PyInstaller builds the exe, so as to inject date/other infos into it. | ||
*.manifest | ||
*.spec | ||
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# Installer logs | ||
pip-log.txt | ||
pip-delete-this-directory.txt | ||
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# Unit test / coverage reports | ||
htmlcov/ | ||
.tox/ | ||
.nox/ | ||
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.cache | ||
nosetests.xml | ||
coverage.xml | ||
*.cover | ||
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# Translations | ||
*.mo | ||
*.pot | ||
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# Django stuff: | ||
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# Scrapy stuff: | ||
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# Sphinx documentation | ||
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# PyBuilder | ||
target/ | ||
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# Jupyter Notebook | ||
.ipynb_checkpoints | ||
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# IPython | ||
profile_default/ | ||
ipython_config.py | ||
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# pyenv | ||
.python-version | ||
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# pipenv | ||
# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. | ||
# However, in case of collaboration, if having platform-specific dependencies or dependencies | ||
# having no cross-platform support, pipenv may install dependencies that don't work, or not | ||
# install all needed dependencies. | ||
#Pipfile.lock | ||
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# PEP 582; used by e.g. github.com/David-OConnor/pyflow | ||
__pypackages__/ | ||
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# Celery stuff | ||
celerybeat-schedule | ||
celerybeat.pid | ||
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# SageMath parsed files | ||
*.sage.py | ||
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# mypy | ||
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dmypy.json | ||
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# What is BLEU | ||
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[BLEU (BiLingual Evaluation Understudy)](https://en.wikipedia.org/wiki/BLEU) is a metric for automatically evaluating machine-translated text. The BLEU score is a number between zero and one that measures the similarity of the machine-translated text to a set of high quality reference translations. A value of 0 means that the machine-translated output has no overlap with the reference translation (low quality) while a value of 1 means there is perfect overlap with the reference translations (high quality). | ||
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It has been shown that BLEU scores correlate well with human judgment of translation quality. Note that even human translators do not achieve a perfect score of 1.0. | ||
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BLEU scores are expressed as a percentage rather than a decimal between 0 and 1. | ||
Trying to compare BLEU scores across different corpora and languages is strongly discouraged. Even comparing BLEU scores for the same corpus but with different numbers of reference translations can be highly misleading. | ||
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However, as a rough guideline, the following interpretation of BLEU scores (expressed as percentages rather than decimals) might be helpful. | ||
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BLEU Score | Interpretation | ||
--- | --- | ||
< 10 | Almost useless | ||
10 - 19 | Hard to get the gist | ||
20 - 29 | The gist is clear, but has significant grammatical errors | ||
30 - 40 | Understandable to good translations | ||
40 - 50 | High quality translations | ||
50 - 60 | Very high quality, adequate, and fluent translations | ||
\> 60 | Quality often better than human | ||
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[More mathematical details](https://cloud.google.com/translate/automl/docs/evaluate#the_mathematical_details) | ||
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Source: https://cloud.google.com/translate/automl/docs/evaluate#bleu | ||
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BLEU is the most popular becnhmark in academia, so using BLEU allows us also to compare with reserach papers results and competitions (see [Conference on Machine Translation Conference (WMT)](http://statmt.org/wmt21/)). | ||
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Read [this article](https://www.rws.com/blog/understanding-mt-quality-bleu-scores/) to better understand what BLEU is and why it is not perfect. | ||
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# What are these benchmarks | ||
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## Translators | ||
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1. **bergamot** - uses compiled [bergamot-translator](https://github.com/mozilla/bergamot-translator) (wrapper for marian that is used by Firefox Translations web extension) | ||
2. **marian** - uses compiled [marian](https://github.com/marian-nmt/marian-dev) (translation engine bergamot-translator is based on) | ||
3. **google** - uses Google Translation [API](https://cloud.google.com/translate) | ||
4. **microsoft** - uses Azure Cognitive Services Translator [API](https://azure.microsoft.com/en-us/services/cognitive-services/translator/) | ||
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Translation quality of Marian and Bergamot is supposed to be very similar. | ||
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## Method | ||
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We use official WMT ([Conference on Machine Translation](http://statmt.org/wmt21/)) parallel datasets. Available datsets are discovered automatically based on a language pair. | ||
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We perform translation from source to target langauge using one of 4 translation systems and then compare the result with the dataset reference and calculate BLEU score. | ||
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Evaluation is done using [SacreBLEU](https://github.com/mjpost/sacrebleu) tool which is reliable and widely used in academic world. | ||
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Both absolute and relative differences in BLEU scores between Bergamot and other systems are reported. | ||
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# Evaluation results | ||
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`avg` = average on all datasets | ||
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## avg | ||
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| Translator/Dataset | ru-en | | ||
| --- | --- | | ||
| bergamot | 30.91 | | ||
| google | 36.59 (+5.68, +18.37%) | | ||
| microsoft | 37.03 (+6.12, +19.81%) | | ||
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![Results](img/avg.png) | ||
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## ru-en | ||
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| Translator/Dataset | mtedx_test | wmt19 | wmt17 | wmt14 | wmt15 | wmt16 | wmt13 | wmt18 | wmt20 | | ||
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | ||
| bergamot | 23.50 | 37.00 | 33.80 | 34.60 | 30.60 | 29.60 | 26.90 | 28.80 | 33.40 | | ||
| google | 25.00 (+1.50, +6.38%) | 42.40 (+5.40, +14.59%) | 41.50 (+7.70, +22.78%) | 41.20 (+6.60, +19.08%) | 37.50 (+6.90, +22.55%) | 36.60 (+7.00, +23.65%) | 31.40 (+4.50, +16.73%) | 36.00 (+7.20, +25.00%) | 37.70 (+4.30, +12.87%) | | ||
| microsoft | 26.10 (+2.60, +11.06%) | 42.60 (+5.60, +15.14%) | 41.60 (+7.80, +23.08%) | 41.70 (+7.10, +20.52%) | 37.80 (+7.20, +23.53%) | 37.60 (+8.00, +27.03%) | 31.20 (+4.30, +15.99%) | 36.90 (+8.10, +28.12%) | 37.80 (+4.40, +13.17%) | | ||
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![Results](img/ru-en.png) |
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