# Natural Language Toolkit: Machine Translation # # Copyright (C) 2001-2024 NLTK Project # Author: Uday Krishna # Contributor: Tom Aarsen # URL: # For license information, see LICENSE.TXT from itertools import chain, product from typing import Callable, Iterable, List, Tuple from nltk.corpus import WordNetCorpusReader, wordnet from nltk.stem.api import StemmerI from nltk.stem.porter import PorterStemmer def _generate_enums( hypothesis: Iterable[str], reference: Iterable[str], preprocess: Callable[[str], str] = str.lower, ) -> Tuple[List[Tuple[int, str]], List[Tuple[int, str]]]: """ Takes in pre-tokenized inputs for hypothesis and reference and returns enumerated word lists for each of them :param hypothesis: pre-tokenized hypothesis :param reference: pre-tokenized reference :preprocess: preprocessing method (default str.lower) :return: enumerated words list """ if isinstance(hypothesis, str): raise TypeError( f'"hypothesis" expects pre-tokenized hypothesis (Iterable[str]): {hypothesis}' ) if isinstance(reference, str): raise TypeError( f'"reference" expects pre-tokenized reference (Iterable[str]): {reference}' ) enum_hypothesis_list = list(enumerate(map(preprocess, hypothesis))) enum_reference_list = list(enumerate(map(preprocess, reference))) return enum_hypothesis_list, enum_reference_list def exact_match( hypothesis: Iterable[str], reference: Iterable[str] ) -> Tuple[List[Tuple[int, int]], List[Tuple[int, str]], List[Tuple[int, str]]]: """ matches exact words in hypothesis and reference and returns a word mapping based on the enumerated word id between hypothesis and reference :param hypothesis: pre-tokenized hypothesis :param reference: pre-tokenized reference :return: enumerated matched tuples, enumerated unmatched hypothesis tuples, enumerated unmatched reference tuples """ enum_hypothesis_list, enum_reference_list = _generate_enums(hypothesis, reference) return _match_enums(enum_hypothesis_list, enum_reference_list) def _match_enums( enum_hypothesis_list: List[Tuple[int, str]], enum_reference_list: List[Tuple[int, str]], ) -> Tuple[List[Tuple[int, int]], List[Tuple[int, str]], List[Tuple[int, str]]]: """ matches exact words in hypothesis and reference and returns a word mapping between enum_hypothesis_list and enum_reference_list based on the enumerated word id. :param enum_hypothesis_list: enumerated hypothesis list :param enum_reference_list: enumerated reference list :return: enumerated matched tuples, enumerated unmatched hypothesis tuples, enumerated unmatched reference tuples """ word_match = [] for i in range(len(enum_hypothesis_list))[::-1]: for j in range(len(enum_reference_list))[::-1]: if enum_hypothesis_list[i][1] == enum_reference_list[j][1]: word_match.append( (enum_hypothesis_list[i][0], enum_reference_list[j][0]) ) enum_hypothesis_list.pop(i) enum_reference_list.pop(j) break return word_match, enum_hypothesis_list, enum_reference_list def _enum_stem_match( enum_hypothesis_list: List[Tuple[int, str]], enum_reference_list: List[Tuple[int, str]], stemmer: StemmerI = PorterStemmer(), ) -> Tuple[List[Tuple[int, int]], List[Tuple[int, str]], List[Tuple[int, str]]]: """ Stems each word and matches them in hypothesis and reference and returns a word mapping between enum_hypothesis_list and enum_reference_list based on the enumerated word id. The function also returns a enumerated list of unmatched words for hypothesis and reference. :param enum_hypothesis_list: enumerated hypothesis list :param enum_reference_list: enumerated reference list :param stemmer: nltk.stem.api.StemmerI object (default PorterStemmer()) :return: enumerated matched tuples, enumerated unmatched hypothesis tuples, enumerated unmatched reference tuples """ stemmed_enum_hypothesis_list = [ (word_pair[0], stemmer.stem(word_pair[1])) for word_pair in enum_hypothesis_list ] stemmed_enum_reference_list = [ (word_pair[0], stemmer.stem(word_pair[1])) for word_pair in enum_reference_list ] return _match_enums(stemmed_enum_hypothesis_list, stemmed_enum_reference_list) def stem_match( hypothesis: Iterable[str], reference: Iterable[str], stemmer: StemmerI = PorterStemmer(), ) -> Tuple[List[Tuple[int, int]], List[Tuple[int, str]], List[Tuple[int, str]]]: """ Stems each word and matches them in hypothesis and reference and returns a word mapping between hypothesis and reference :param hypothesis: pre-tokenized hypothesis :param reference: pre-tokenized reference :param stemmer: nltk.stem.api.StemmerI object (default PorterStemmer()) :return: enumerated matched tuples, enumerated unmatched hypothesis tuples, enumerated unmatched reference tuples """ enum_hypothesis_list, enum_reference_list = _generate_enums(hypothesis, reference) return _enum_stem_match(enum_hypothesis_list, enum_reference_list, stemmer=stemmer) def _enum_wordnetsyn_match( enum_hypothesis_list: List[Tuple[int, str]], enum_reference_list: List[Tuple[int, str]], wordnet: WordNetCorpusReader = wordnet, ) -> Tuple[List[Tuple[int, int]], List[Tuple[int, str]], List[Tuple[int, str]]]: """ Matches each word in reference to a word in hypothesis if any synonym of a hypothesis word is the exact match to the reference word. :param enum_hypothesis_list: enumerated hypothesis list :param enum_reference_list: enumerated reference list :param wordnet: a wordnet corpus reader object (default nltk.corpus.wordnet) """ word_match = [] for i in range(len(enum_hypothesis_list))[::-1]: hypothesis_syns = set( chain.from_iterable( ( lemma.name() for lemma in synset.lemmas() if lemma.name().find("_") < 0 ) for synset in wordnet.synsets(enum_hypothesis_list[i][1]) ) ).union({enum_hypothesis_list[i][1]}) for j in range(len(enum_reference_list))[::-1]: if enum_reference_list[j][1] in hypothesis_syns: word_match.append( (enum_hypothesis_list[i][0], enum_reference_list[j][0]) ) enum_hypothesis_list.pop(i) enum_reference_list.pop(j) break return word_match, enum_hypothesis_list, enum_reference_list def wordnetsyn_match( hypothesis: Iterable[str], reference: Iterable[str], wordnet: WordNetCorpusReader = wordnet, ) -> Tuple[List[Tuple[int, int]], List[Tuple[int, str]], List[Tuple[int, str]]]: """ Matches each word in reference to a word in hypothesis if any synonym of a hypothesis word is the exact match to the reference word. :param hypothesis: pre-tokenized hypothesis :param reference: pre-tokenized reference :param wordnet: a wordnet corpus reader object (default nltk.corpus.wordnet) :return: list of mapped tuples """ enum_hypothesis_list, enum_reference_list = _generate_enums(hypothesis, reference) return _enum_wordnetsyn_match( enum_hypothesis_list, enum_reference_list, wordnet=wordnet ) def _enum_align_words( enum_hypothesis_list: List[Tuple[int, str]], enum_reference_list: List[Tuple[int, str]], stemmer: StemmerI = PorterStemmer(), wordnet: WordNetCorpusReader = wordnet, ) -> Tuple[List[Tuple[int, int]], List[Tuple[int, str]], List[Tuple[int, str]]]: """ Aligns/matches words in the hypothesis to reference by sequentially applying exact match, stemmed match and wordnet based synonym match. in case there are multiple matches the match which has the least number of crossing is chosen. Takes enumerated list as input instead of string input :param enum_hypothesis_list: enumerated hypothesis list :param enum_reference_list: enumerated reference list :param stemmer: nltk.stem.api.StemmerI object (default PorterStemmer()) :param wordnet: a wordnet corpus reader object (default nltk.corpus.wordnet) :return: sorted list of matched tuples, unmatched hypothesis list, unmatched reference list """ exact_matches, enum_hypothesis_list, enum_reference_list = _match_enums( enum_hypothesis_list, enum_reference_list ) stem_matches, enum_hypothesis_list, enum_reference_list = _enum_stem_match( enum_hypothesis_list, enum_reference_list, stemmer=stemmer ) wns_matches, enum_hypothesis_list, enum_reference_list = _enum_wordnetsyn_match( enum_hypothesis_list, enum_reference_list, wordnet=wordnet ) return ( sorted( exact_matches + stem_matches + wns_matches, key=lambda wordpair: wordpair[0] ), enum_hypothesis_list, enum_reference_list, ) def align_words( hypothesis: Iterable[str], reference: Iterable[str], stemmer: StemmerI = PorterStemmer(), wordnet: WordNetCorpusReader = wordnet, ) -> Tuple[List[Tuple[int, int]], List[Tuple[int, str]], List[Tuple[int, str]]]: """ Aligns/matches words in the hypothesis to reference by sequentially applying exact match, stemmed match and wordnet based synonym match. In case there are multiple matches the match which has the least number of crossing is chosen. :param hypothesis: pre-tokenized hypothesis :param reference: pre-tokenized reference :param stemmer: nltk.stem.api.StemmerI object (default PorterStemmer()) :param wordnet: a wordnet corpus reader object (default nltk.corpus.wordnet) :return: sorted list of matched tuples, unmatched hypothesis list, unmatched reference list """ enum_hypothesis_list, enum_reference_list = _generate_enums(hypothesis, reference) return _enum_align_words( enum_hypothesis_list, enum_reference_list, stemmer=stemmer, wordnet=wordnet ) def _count_chunks(matches: List[Tuple[int, int]]) -> int: """ Counts the fewest possible number of chunks such that matched unigrams of each chunk are adjacent to each other. This is used to calculate the fragmentation part of the metric. :param matches: list containing a mapping of matched words (output of align_words) :return: Number of chunks a sentence is divided into post alignment """ i = 0 chunks = 1 while i < len(matches) - 1: if (matches[i + 1][0] == matches[i][0] + 1) and ( matches[i + 1][1] == matches[i][1] + 1 ): i += 1 continue i += 1 chunks += 1 return chunks def single_meteor_score( reference: Iterable[str], hypothesis: Iterable[str], preprocess: Callable[[str], str] = str.lower, stemmer: StemmerI = PorterStemmer(), wordnet: WordNetCorpusReader = wordnet, alpha: float = 0.9, beta: float = 3.0, gamma: float = 0.5, ) -> float: """ Calculates METEOR score for single hypothesis and reference as per "Meteor: An Automatic Metric for MT Evaluation with HighLevels of Correlation with Human Judgments" by Alon Lavie and Abhaya Agarwal, in Proceedings of ACL. https://www.cs.cmu.edu/~alavie/METEOR/pdf/Lavie-Agarwal-2007-METEOR.pdf >>> hypothesis1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which', 'ensures', 'that', 'the', 'military', 'always', 'obeys', 'the', 'commands', 'of', 'the', 'party'] >>> reference1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'that', 'ensures', 'that', 'the', 'military', 'will', 'forever', 'heed', 'Party', 'commands'] >>> round(single_meteor_score(reference1, hypothesis1),4) 0.6944 If there is no words match during the alignment the method returns the score as 0. We can safely return a zero instead of raising a division by zero error as no match usually implies a bad translation. >>> round(single_meteor_score(['this', 'is', 'a', 'cat'], ['non', 'matching', 'hypothesis']),4) 0.0 :param reference: pre-tokenized reference :param hypothesis: pre-tokenized hypothesis :param preprocess: preprocessing function (default str.lower) :param stemmer: nltk.stem.api.StemmerI object (default PorterStemmer()) :param wordnet: a wordnet corpus reader object (default nltk.corpus.wordnet) :param alpha: parameter for controlling relative weights of precision and recall. :param beta: parameter for controlling shape of penalty as a function of as a function of fragmentation. :param gamma: relative weight assigned to fragmentation penalty. :return: The sentence-level METEOR score. """ enum_hypothesis, enum_reference = _generate_enums( hypothesis, reference, preprocess=preprocess ) translation_length = len(enum_hypothesis) reference_length = len(enum_reference) matches, _, _ = _enum_align_words( enum_hypothesis, enum_reference, stemmer=stemmer, wordnet=wordnet ) matches_count = len(matches) try: precision = float(matches_count) / translation_length recall = float(matches_count) / reference_length fmean = (precision * recall) / (alpha * precision + (1 - alpha) * recall) chunk_count = float(_count_chunks(matches)) frag_frac = chunk_count / matches_count except ZeroDivisionError: return 0.0 penalty = gamma * frag_frac**beta return (1 - penalty) * fmean def meteor_score( references: Iterable[Iterable[str]], hypothesis: Iterable[str], preprocess: Callable[[str], str] = str.lower, stemmer: StemmerI = PorterStemmer(), wordnet: WordNetCorpusReader = wordnet, alpha: float = 0.9, beta: float = 3.0, gamma: float = 0.5, ) -> float: """ Calculates METEOR score for hypothesis with multiple references as described in "Meteor: An Automatic Metric for MT Evaluation with HighLevels of Correlation with Human Judgments" by Alon Lavie and Abhaya Agarwal, in Proceedings of ACL. https://www.cs.cmu.edu/~alavie/METEOR/pdf/Lavie-Agarwal-2007-METEOR.pdf In case of multiple references the best score is chosen. This method iterates over single_meteor_score and picks the best pair among all the references for a given hypothesis >>> hypothesis1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which', 'ensures', 'that', 'the', 'military', 'always', 'obeys', 'the', 'commands', 'of', 'the', 'party'] >>> hypothesis2 = ['It', 'is', 'to', 'insure', 'the', 'troops', 'forever', 'hearing', 'the', 'activity', 'guidebook', 'that', 'party', 'direct'] >>> reference1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'that', 'ensures', 'that', 'the', 'military', 'will', 'forever', 'heed', 'Party', 'commands'] >>> reference2 = ['It', 'is', 'the', 'guiding', 'principle', 'which', 'guarantees', 'the', 'military', 'forces', 'always', 'being', 'under', 'the', 'command', 'of', 'the', 'Party'] >>> reference3 = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the', 'army', 'always', 'to', 'heed', 'the', 'directions', 'of', 'the', 'party'] >>> round(meteor_score([reference1, reference2, reference3], hypothesis1),4) 0.6944 If there is no words match during the alignment the method returns the score as 0. We can safely return a zero instead of raising a division by zero error as no match usually implies a bad translation. >>> round(meteor_score([['this', 'is', 'a', 'cat']], ['non', 'matching', 'hypothesis']),4) 0.0 :param references: pre-tokenized reference sentences :param hypothesis: a pre-tokenized hypothesis sentence :param preprocess: preprocessing function (default str.lower) :param stemmer: nltk.stem.api.StemmerI object (default PorterStemmer()) :param wordnet: a wordnet corpus reader object (default nltk.corpus.wordnet) :param alpha: parameter for controlling relative weights of precision and recall. :param beta: parameter for controlling shape of penalty as a function of as a function of fragmentation. :param gamma: relative weight assigned to fragmentation penalty. :return: The sentence-level METEOR score. """ return max( single_meteor_score( reference, hypothesis, preprocess=preprocess, stemmer=stemmer, wordnet=wordnet, alpha=alpha, beta=beta, gamma=gamma, ) for reference in references )