Source code for nltk.translate.chrf_score

# -*- coding: utf-8 -*-
# Natural Language Toolkit: ChrF score
#
# Copyright (C) 2001-2018 NLTK Project
# Authors: Maja Popovic
# Contributors: Liling Tan
# URL: <http://nltk.org/>
# For license information, see LICENSE.TXT

""" ChrF score implementation """
from __future__ import division
from collections import Counter

from nltk.util import ngrams, everygrams

[docs]def sentence_chrf(reference, hypothesis, min_len=1, max_len=6, beta=3.0): """ Calculates the sentence level CHRF (Character n-gram F-score) described in - Maja Popovic. 2015. CHRF: Character n-gram F-score for Automatic MT Evaluation. In Proceedings of the 10th Workshop on Machine Translation. http://www.statmt.org/wmt15/pdf/WMT49.pdf - Maja Popovic. 2016. CHRF Deconstructed: β Parameters and n-gram Weights. In Proceedings of the 1st Conference on Machine Translation. http://www.statmt.org/wmt16/pdf/W16-2341.pdf Unlike multi-reference BLEU, CHRF only supports a single reference. An example from the original BLEU paper http://www.aclweb.org/anthology/P02-1040.pdf >>> ref1 = str('It is a guide to action that ensures that the military ' ... 'will forever heed Party commands').split() >>> hyp1 = str('It is a guide to action which ensures that the military ' ... 'always obeys the commands of the party').split() >>> hyp2 = str('It is to insure the troops forever hearing the activity ' ... 'guidebook that party direct').split() >>> sentence_chrf(ref1, hyp1) # doctest: +ELLIPSIS 0.6768... >>> sentence_chrf(ref1, hyp2) # doctest: +ELLIPSIS 0.4201... The infamous "the the the ... " example >>> ref = 'the cat is on the mat'.split() >>> hyp = 'the the the the the the the'.split() >>> sentence_chrf(ref, hyp) # doctest: +ELLIPSIS 0.2530... An example to show that this function allows users to use strings instead of tokens, i.e. list(str) as inputs. >>> ref1 = str('It is a guide to action that ensures that the military ' ... 'will forever heed Party commands') >>> hyp1 = str('It is a guide to action which ensures that the military ' ... 'always obeys the commands of the party') >>> sentence_chrf(ref1, hyp1) # doctest: +ELLIPSIS 0.6768... >>> type(ref1) == type(hyp1) == str True >>> sentence_chrf(ref1.split(), hyp1.split()) # doctest: +ELLIPSIS 0.6768... To skip the unigrams and only use 2- to 3-grams: >>> sentence_chrf(ref1, hyp1, min_len=2, max_len=3) # doctest: +ELLIPSIS 0.7018... :param references: reference sentence :type references: list(str) / str :param hypothesis: a hypothesis sentence :type hypothesis: list(str) / str :param min_len: The minimum order of n-gram this function should extract. :type min_len: int :param max_len: The maximum order of n-gram this function should extract. :type max_len: int :param beta: the parameter to assign more importance to recall over precision :type beta: float :return: the sentence level CHRF score. :rtype: float """ return corpus_chrf([reference], [hypothesis], min_len, max_len, beta=beta)
[docs]def corpus_chrf(list_of_references, hypotheses, min_len=1, max_len=6, beta=3.0): """ Calculates the corpus level CHRF (Character n-gram F-score), it is the micro-averaged value of the sentence/segment level CHRF score. CHRF only supports a single reference. >>> ref1 = str('It is a guide to action that ensures that the military ' ... 'will forever heed Party commands').split() >>> ref2 = str('It is the guiding principle which guarantees the military ' ... 'forces always being under the command of the Party').split() >>> >>> hyp1 = str('It is a guide to action which ensures that the military ' ... 'always obeys the commands of the party').split() >>> hyp2 = str('It is to insure the troops forever hearing the activity ' ... 'guidebook that party direct') >>> corpus_chrf([ref1, ref2, ref1, ref2], [hyp1, hyp2, hyp2, hyp1]) # doctest: +ELLIPSIS 0.4915... :param references: a corpus of list of reference sentences, w.r.t. hypotheses :type references: list(list(str)) / list(str) :param hypotheses: a list of hypothesis sentences :type hypotheses: list(list(str)) / list(str) :param min_len: The minimum order of n-gram this function should extract. :type min_len: int :param max_len: The maximum order of n-gram this function should extract. :type max_len: int :param beta: the parameter to assign more importance to recall over precision :type beta: float :return: the sentence level CHRF score. :rtype: float """ assert len(list_of_references) == len(hypotheses), "The number of hypotheses and their references should be the same" # Iterate through each hypothesis and their corresponding references. for reference, hypothesis in zip(list_of_references, hypotheses): # Cheating condition to allow users to input strings instead of tokens. if type(reference) and type(hypothesis) != str: reference, hypothesis = ' '.join(reference), ' '.join(hypothesis) # For each order of ngram, calculate the no. of ngram matches and # keep track of no. of ngram in references. ref_ngrams = Counter(everygrams(reference, min_len, max_len)) hyp_ngrams = Counter(everygrams(hypothesis, min_len, max_len)) overlap_ngrams = ref_ngrams & hyp_ngrams tp = sum(overlap_ngrams.values()) # True positives. tpfp = sum(hyp_ngrams.values()) # True positives + False positives. tffn = sum(ref_ngrams.values()) # True posities + False negatives. precision = tp / tpfp recall = tp / tffn factor = beta**2 score = (1+ factor ) * (precision * recall) / ( factor * precision + recall) return score