hLepor 0.0.1

This is Python port of original algorithm by Hang Lifeng

Library to calculate hLEPOR score (harmonic mean of enhanced Length Penalty, Precision, n-gram Position difference Penalty and Recall) has been created as port from Perl on materials of the following atricle by Aaron Li-Feng Han, Derek F. Wong, Lidia S. Chao, Liangye He Yi Lu, Junwen Xing, and Xiaodong Zeng. 2013. "Language-independent Model for Machine Translation Evaluation with Reinforced Factors". In Proceedings of the XIV Machine Translation Summit.

All hLepor score calculation functions take mandatory and optional parameters for input; mandatory parameters are: reference (ideal translation), hypothesis - new translation which has to be compared with reference.

Optional parameters are:

• preprocess is a function to preprocess strings, default is str.lower().

• separate_punctuation allows different tokenization options: by default standard word_tokenize() function from nltk.tokenize is used, for this option you can specify the language (default is English), if separate_punctuation = False, sentence is tikenized by spaces.

Other optional parameters control hLepor algorithm:

• alpha and beta -- recall and precision weights, respectively, to calculate weighted Harmonic mean of precision and recall;
• n -- number of words in vicinity of current word in N-gram word alignment algorithm;
• weight_elp, weight_pos, weight_pr -- weigths for enhanced length penalty, N-gram Position Difference Penalty and weighted Harmonic mean of precision and recall for hLepor calculation.

Main functions:

1. To calculate hLepor on one pair of sentences you need to pass these strings to single_hlepor_score function:

reference = 'It is a guide to action that ensures that the military will forever heed Party commands'
hypothesis = 'It is a guide to action which ensures that the military always obeys the commands of the party'
hLepor_value = single_hlepor_score(reference, hypothesis)
round(hLepor_value, 4)

The result if 0.7842.

2. To calculate hLepor on a set of sentences they need to be passed to hLepor as a list of strings:

reference = ['It is a guide to action that ensures that the military will forever heed Party commands',
             'It is the practical guide for the army always to heed the directions of the party']
hypothesis = ['It is a guide to action which ensures that the military always obeys the commands of the party',
              'It is to insure the troops forever hearing the activity guidebook that party direct']
hLepor_value = hlepor_score(reference, hypothesis)
round(hLepor_value, 4)

This code will calculate hLepor on each pair of sentences and mean value will be calculated, the result should be 0.6214.