Changing BLEU tokenizer behavior and making the default tokenizer 13a #20
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lvwerra
reviewed
May 2, 2022
lhoestq
reviewed
May 2, 2022
| ... ] | ||
| >>> bleu = evaluate.load_metric("bleu") | ||
| >>> results = bleu.compute(predictions=predictions, references=references) | ||
| >>> print(results) | ||
| {'bleu': 0.6370964381207871, 'precisions': [0.8333333333333334, 0.75, 1.0, 1.0], 'brevity_penalty': 0.7165313105737893, 'length_ratio': 0.75, 'translation_length': 6, 'reference_length': 8} | ||
| {'bleu': 1.0, 'precisions': [1.0, 1.0, 1.0, 1.0], 'brevity_penalty': 1.0, 'length_ratio': 1.1666666666666667, 'translation_length': 7, 'reference_length': 6} |
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Any idea why the results are not the same ?
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I added a second reference compared to the previous example (to illustrate the functionality).
I triple-checked though, and the results are the same for the previous version and the current version of the code! 🤗
Co-authored-by: Quentin Lhoest <42851186+lhoestq@users.noreply.github.com>
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As it was previously discussed in an issue from 2020, inconsistent tokenization in BLEU can cause reproducibility issues. The proposed solution was to use tokenizer
13a, such as is the default case for SacreBLEU and WMT.After discussion with @lhoestq , I made this the default dehavior of the BLEU implementation, however making it possible to use other tokeniizers such as NLTK's
word_tokenize.I also updated the README to reflect these changes and to further discuss the impact that tokenization can have on reproducibility of BLEU scores.
Please let me know what you think, @lvwerra and @lhoestq !
cc @thomwolf cause you were involved in the original discussion in the issue I linked above.