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69 lines
2.3 KiB
69 lines
2.3 KiB
"""External Scorer for Beam Search Decoder."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import os
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import kenlm
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import numpy as np
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class Scorer(object):
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"""External scorer to evaluate a prefix or whole sentence in
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beam search decoding, including the score from n-gram language
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model and word count.
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:param alpha: Parameter associated with language model. Don't use
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language model when alpha = 0.
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:type alpha: float
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:param beta: Parameter associated with word count. Don't use word
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count when beta = 0.
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:type beta: float
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:model_path: Path to load language model.
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:type model_path: basestring
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"""
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def __init__(self, alpha, beta, model_path):
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self._alpha = alpha
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self._beta = beta
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if not os.path.isfile(model_path):
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raise IOError("Invaid language model path: %s" % model_path)
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self._language_model = kenlm.LanguageModel(model_path)
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# n-gram language model scoring
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def _language_model_score(self, sentence):
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#log10 prob of last word
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log_cond_prob = list(
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self._language_model.full_scores(sentence, eos=False))[-1][0]
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return np.power(10, log_cond_prob)
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# word insertion term
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def _word_count(self, sentence):
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words = sentence.strip().split(' ')
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return len(words)
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# reset alpha and beta
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def reset_params(self, alpha, beta):
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self._alpha = alpha
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self._beta = beta
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# execute evaluation
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def __call__(self, sentence, log=False):
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"""Evaluation function, gathering all the different scores
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and return the final one.
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:param sentence: The input sentence for evalutation
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:type sentence: basestring
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:param log: Whether return the score in log representation.
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:type log: bool
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:return: Evaluation score, in the decimal or log.
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:rtype: float
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"""
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lm = self._language_model_score(sentence)
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word_cnt = self._word_count(sentence)
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if log == False:
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score = np.power(lm, self._alpha) * np.power(word_cnt, self._beta)
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else:
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score = self._alpha * np.log(lm) + self._beta * np.log(word_cnt)
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return score
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