This is a fast multithreaded C++ implementation of NLTK BLEU with Python wrapper; computing BLEU and SelfBLEU scores for a fixed reference set. It can return (Self)BLEU for different (max) n-grams simultaneously and efficiently (e.g. BLEU-2, BLEU-3, etc.).
The installation requires c++11.
The requirements.txt file is the required python packages to run the test_cases.py file.
Installing PyPI latest stable release:
pip install --user fast-bleuAs the macOS uses clang and it does not support OpenMP, one workaround is to first install gcc with brew install gcc. After that, gcc specific binaries will be added (for example, it will be maybe gcc-10
and g++-10).
To change the default compiler, an option to the installation command is added, so you can install the PyPI latest stable release with the following command:
pip install --user fast-bleu --install-option="--CC=<path-to-gcc>" --install-option="--CXX=<path-to-g++>"Not tested yet!
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.
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},
}
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