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import json, itertools, pyter
from nltk.translate.bleu_score import SmoothingFunction, corpus_bleu
class NLPFactGenerator:
def __init__(self):
self.gen_fact_list = []
self.evidence_list = []
def _split_into_words(self, sentences):
return list(itertools.chain(*[_.split(" ") for _ in sentences]))
def _get_word_ngrams(self, n, sentences):
assert len(sentences) > 0
assert n > 0
words = self._split_into_words(sentences)
return self._get_ngrams(n, words)
def _get_ngrams(self, n, text):
ngram_set = set()
text_length = len(text)
max_index_ngram_start = text_length - n
for i in range(max_index_ngram_start + 1):
ngram_set.add(tuple(text[i:i + n]))
return ngram_set
def load_data(self, filename):
with open(filename, "r") as infile:
self.data = json.load(infile)
def get_title_evidence_generated_facts(self):
titles = []
evidences = []
generated_facts = []
for entry in self.data:
titles.append(entry["title"])
evidences.append(entry["evidence"])
generated_facts.append(entry["generated_fact"])
return evidences, generated_facts
def ter(self):
ref, gen = self.get_title_evidence_generated_facts()
if len(ref) == 1:
total_score = pyter.ter(gen[0].split(), ref[0].split())
else:
total_score = 0
for i in range(len(gen)):
total_score = total_score + pyter.ter(gen[i].split(), ref[i].split())
total_score = total_score/len(gen)
return total_score
def bleu(self):
evidence_list, gen_fact_list = self.get_title_evidence_generated_facts()
ref_bleu = []
gen_bleu = []
for l in evidence_list:
gen_bleu.append(l.split())
for i,l in enumerate(gen_fact_list):
ref_bleu.append([l.split()])
cc = SmoothingFunction()
score_bleu = corpus_bleu(ref_bleu, gen_bleu, weights=(0, 1, 0, 0), smoothing_function=cc.method4)
return score_bleu
def rouge_one(self,n=3):
evidence_list, gen_fact_list = self.get_title_evidence_generated_facts()
evaluated_ngrams = self._get_word_ngrams(n, evidence_list)
reference_ngrams = self._get_word_ngrams(n, gen_fact_list)
reference_count = len(reference_ngrams)
evaluated_count = len(evaluated_ngrams)
overlapping_ngrams = evaluated_ngrams.intersection(reference_ngrams)
overlapping_count = len(overlapping_ngrams)
if evaluated_count == 0:
precision = 0.0
else:
precision = overlapping_count / evaluated_count
if reference_count == 0:
recall = 0.0
else:
recall = overlapping_count / reference_count
f1_score = 2.0 * ((precision * recall) / (precision + recall + 1e-8))
return recall
if __name__ == "__main__":
fact_generator = NLPFactGenerator()
fact_generator.load_data("generated_facts_xlsum.json")
rouge_one_score = fact_generator.rouge_one()
blue_score = fact_generator.bleu()
print(blue_score)
print(rouge_one_score)
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