FastBLEU 0.0.41

A fast multithreaded C++ implementation of nltk BLEU.

CAUTION

This package is renamed to "fast-bleu".

Please check the new package to get updated versions.

FastBLEU Package (DEPRECATED)

This is a fast multithreaded C++ implementation of NLTK BLEU; computing BLEU and SelfBLEU score for a fixed reference set. It can return (Self)BLEU for different (max) n-grams simultaneously and efficiently (e.g. BLEU-2, BLEU-3 and etc.).

Installation

PyPI latest stable release

pip install --user FastBLEU

Sample Usage

Here is an example to compute BLEU-2, BLEU-3, SelfBLEU-2 and SelfBLEU-3:

>>> from fast_bleu import BLEU, SelfBLEU
>>> ref1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',
...          'ensures', 'that', 'the', 'military', 'will', 'forever',
...          'heed', 'Party', 'commands']
>>> ref2 = ['It', 'is', 'the', 'guiding', 'principle', 'which',
...          'guarantees', 'the', 'military', 'forces', 'always',
...          'being', 'under', 'the', 'command', 'of', 'the', 'Party']
>>> ref3 = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',
...          'army', 'always', 'to', 'heed', 'the', 'directions',
...          'of', 'the', 'party']

>>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',
...         'ensures', 'that', 'the', 'military', 'always',
...         'obeys', 'the', 'commands', 'of', 'the', 'party']
>>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',
...         'interested', 'in', 'world', 'history']

>>> list_of_references = [ref1, ref2, ref3]
>>> hypotheses = [hyp1, hyp2]
>>> weights = {'bigram': (1/2., 1/2.), 'trigram': (1/3., 1/3., 1/3.)}

>>> bleu = BLEU(list_of_references, weights)
>>> bleu.get_score(hypotheses)
{'bigram': [0.7453559924999299, 0.0191380231127159], 'trigram': [0.6240726901657495, 0.013720869575946234]}

which means:

• BLEU-2 for hyp1 is 0.7453559924999299

• BLEU-2 for hyp2 is 0.0191380231127159

• BLEU-3 for hyp1 is 0.6240726901657495

• BLEU-3 for hyp2 is 0.013720869575946234

>>> self_bleu = SelfBLEU(list_of_references, weights)
>>> self_bleu.get_score()
{'bigram': [0.25819888974716115, 0.3615507630310936, 0.37080992435478316],
        'trigram': [0.07808966062765045, 0.20140620205719248, 0.21415334758254043]}

which means:

• SelfBLEU-2 for ref1 is 0.25819888974716115

• SelfBLEU-2 for ref2 is 0.3615507630310936

• SelfBLEU-2 for ref3 is 0.37080992435478316

• SelfBLEU-3 for ref1 is 0.07808966062765045

• SelfBLEU-3 for ref2 is 0.20140620205719248

• SelfBLEU-3 for ref3 is 0.21415334758254043

Caution Each token of reference set is converted to string format during computation.

For further details, refer to the documentation provided in the source codes.

Citation

Please cite our paper if it helps with your research.

• ACL Anthology: https://www.aclweb.org/anthology/W19-2311
• Arxiv link: https://arxiv.org/abs/1904.03971

@inproceedings{alihosseini-etal-2019-jointly,
    title = {Jointly Measuring Diversity and Quality in Text Generation Models},
    author = {Alihosseini, Danial  and
      Montahaei, Ehsan  and
      Soleymani Baghshah, Mahdieh},
    booktitle = {Proceedings of the Workshop on Methods for Optimizing and Evaluating Neural Language Generation},
    month = {jun},
    year = {2019},
    address = {Minneapolis, Minnesota},
    publisher = {Association for Computational Linguistics},
    url = {https://www.aclweb.org/anthology/W19-2311},
    doi = {10.18653/v1/W19-2311},
    pages = {90--98},
}