Source code for nltk.translate.stack_decoder

# -*- coding: utf-8 -*-
# Natural Language Toolkit: Stack decoder
# Copyright (C) 2001-2017 NLTK Project
# Author: Tah Wei Hoon <>
# URL: <>
# For license information, see LICENSE.TXT

A decoder that uses stacks to implement phrase-based translation.

In phrase-based translation, the source sentence is segmented into
phrases of one or more words, and translations for those phrases are
used to build the target sentence.

Hypothesis data structures are used to keep track of the source words
translated so far and the partial output. A hypothesis can be expanded
by selecting an untranslated phrase, looking up its translation in a
phrase table, and appending that translation to the partial output.
Translation is complete when a hypothesis covers all source words.

The search space is huge because the source sentence can be segmented
in different ways, the source phrases can be selected in any order,
and there could be multiple translations for the same source phrase in
the phrase table. To make decoding tractable, stacks are used to limit
the number of candidate hypotheses by doing histogram and/or threshold

Hypotheses with the same number of words translated are placed in the
same stack. In histogram pruning, each stack has a size limit, and
the hypothesis with the lowest score is removed when the stack is full.
In threshold pruning, hypotheses that score below a certain threshold
of the best hypothesis in that stack are removed.

Hypothesis scoring can include various factors such as phrase
translation probability, language model probability, length of
translation, cost of remaining words to be translated, and so on.

Philipp Koehn. 2010. Statistical Machine Translation.
Cambridge University Press, New York.

import warnings
from collections import defaultdict
from math import log

[docs]class StackDecoder(object): """ Phrase-based stack decoder for machine translation >>> from nltk.translate import PhraseTable >>> phrase_table = PhraseTable() >>> phrase_table.add(('niemand',), ('nobody',), log(0.8)) >>> phrase_table.add(('niemand',), ('no', 'one'), log(0.2)) >>> phrase_table.add(('erwartet',), ('expects',), log(0.8)) >>> phrase_table.add(('erwartet',), ('expecting',), log(0.2)) >>> phrase_table.add(('niemand', 'erwartet'), ('one', 'does', 'not', 'expect'), log(0.1)) >>> phrase_table.add(('die', 'spanische', 'inquisition'), ('the', 'spanish', 'inquisition'), log(0.8)) >>> phrase_table.add(('!',), ('!',), log(0.8)) >>> # nltk.model should be used here once it is implemented >>> from collections import defaultdict >>> language_prob = defaultdict(lambda: -999.0) >>> language_prob[('nobody',)] = log(0.5) >>> language_prob[('expects',)] = log(0.4) >>> language_prob[('the', 'spanish', 'inquisition')] = log(0.2) >>> language_prob[('!',)] = log(0.1) >>> language_model = type('',(object,),{'probability_change': lambda self, context, phrase: language_prob[phrase], 'probability': lambda self, phrase: language_prob[phrase]})() >>> stack_decoder = StackDecoder(phrase_table, language_model) >>> stack_decoder.translate(['niemand', 'erwartet', 'die', 'spanische', 'inquisition', '!']) ['nobody', 'expects', 'the', 'spanish', 'inquisition', '!'] """ def __init__(self, phrase_table, language_model): """ :param phrase_table: Table of translations for source language phrases and the log probabilities for those translations. :type phrase_table: PhraseTable :param language_model: Target language model. Must define a ``probability_change`` method that calculates the change in log probability of a sentence, if a given string is appended to it. This interface is experimental and will likely be replaced with nltk.model once it is implemented. :type language_model: object """ self.phrase_table = phrase_table self.language_model = language_model self.word_penalty = 0.0 """ float: Influences the translation length exponentially. If positive, shorter translations are preferred. If negative, longer translations are preferred. If zero, no penalty is applied. """ self.beam_threshold = 0.0 """ float: Hypotheses that score below this factor of the best hypothesis in a stack are dropped from consideration. Value between 0.0 and 1.0. """ self.stack_size = 100 """ int: Maximum number of hypotheses to consider in a stack. Higher values increase the likelihood of a good translation, but increases processing time. """ self.__distortion_factor = 0.5 self.__compute_log_distortion() @property def distortion_factor(self): """ float: Amount of reordering of source phrases. Lower values favour monotone translation, suitable when word order is similar for both source and target languages. Value between 0.0 and 1.0. Default 0.5. """ return self.__distortion_factor @distortion_factor.setter def distortion_factor(self, d): self.__distortion_factor = d self.__compute_log_distortion() def __compute_log_distortion(self): # cache log(distortion_factor) so we don't have to recompute it # when scoring hypotheses if self.__distortion_factor == 0.0: self.__log_distortion_factor = log(1e-9) # 1e-9 is almost zero else: self.__log_distortion_factor = log(self.__distortion_factor)
[docs] def translate(self, src_sentence): """ :param src_sentence: Sentence to be translated :type src_sentence: list(str) :return: Translated sentence :rtype: list(str) """ sentence = tuple(src_sentence) # prevent accidental modification sentence_length = len(sentence) stacks = [_Stack(self.stack_size, self.beam_threshold) for _ in range(0, sentence_length + 1)] empty_hypothesis = _Hypothesis() stacks[0].push(empty_hypothesis) all_phrases = self.find_all_src_phrases(sentence) future_score_table = self.compute_future_scores(sentence) for stack in stacks: for hypothesis in stack: possible_expansions = StackDecoder.valid_phrases(all_phrases, hypothesis) for src_phrase_span in possible_expansions: src_phrase = sentence[src_phrase_span[0]:src_phrase_span[1]] for translation_option in (self.phrase_table. translations_for(src_phrase)): raw_score = self.expansion_score( hypothesis, translation_option, src_phrase_span) new_hypothesis = _Hypothesis( raw_score=raw_score, src_phrase_span=src_phrase_span, trg_phrase=translation_option.trg_phrase, previous=hypothesis ) new_hypothesis.future_score = self.future_score( new_hypothesis, future_score_table, sentence_length) total_words = new_hypothesis.total_translated_words() stacks[total_words].push(new_hypothesis) if not stacks[sentence_length]: warnings.warn('Unable to translate all words. ' 'The source sentence contains words not in ' 'the phrase table') # Instead of returning empty output, perhaps a partial # translation could be returned return [] best_hypothesis = stacks[sentence_length].best() return best_hypothesis.translation_so_far()
[docs] def find_all_src_phrases(self, src_sentence): """ Finds all subsequences in src_sentence that have a phrase translation in the translation table :type src_sentence: tuple(str) :return: Subsequences that have a phrase translation, represented as a table of lists of end positions. For example, if result[2] is [5, 6, 9], then there are three phrases starting from position 2 in ``src_sentence``, ending at positions 5, 6, and 9 exclusive. The list of ending positions are in ascending order. :rtype: list(list(int)) """ sentence_length = len(src_sentence) phrase_indices = [[] for _ in src_sentence] for start in range(0, sentence_length): for end in range(start + 1, sentence_length + 1): potential_phrase = src_sentence[start:end] if potential_phrase in self.phrase_table: phrase_indices[start].append(end) return phrase_indices
[docs] def compute_future_scores(self, src_sentence): """ Determines the approximate scores for translating every subsequence in ``src_sentence`` Future scores can be used a look-ahead to determine the difficulty of translating the remaining parts of a src_sentence. :type src_sentence: tuple(str) :return: Scores of subsequences referenced by their start and end positions. For example, result[2][5] is the score of the subsequence covering positions 2, 3, and 4. :rtype: dict(int: (dict(int): float)) """ scores = defaultdict(lambda: defaultdict(lambda: float('-inf'))) for seq_length in range(1, len(src_sentence) + 1): for start in range(0, len(src_sentence) - seq_length + 1): end = start + seq_length phrase = src_sentence[start:end] if phrase in self.phrase_table: score = self.phrase_table.translations_for( phrase)[0].log_prob # pick best (first) translation # Warning: API of language_model is subject to change score += self.language_model.probability(phrase) scores[start][end] = score # check if a better score can be obtained by combining # two child subsequences for mid in range(start + 1, end): combined_score = (scores[start][mid] + scores[mid][end]) if combined_score > scores[start][end]: scores[start][end] = combined_score return scores
[docs] def future_score(self, hypothesis, future_score_table, sentence_length): """ Determines the approximate score for translating the untranslated words in ``hypothesis`` """ score = 0.0 for span in hypothesis.untranslated_spans(sentence_length): score += future_score_table[span[0]][span[1]] return score
[docs] def expansion_score(self, hypothesis, translation_option, src_phrase_span): """ Calculate the score of expanding ``hypothesis`` with ``translation_option`` :param hypothesis: Hypothesis being expanded :type hypothesis: _Hypothesis :param translation_option: Information about the proposed expansion :type translation_option: PhraseTableEntry :param src_phrase_span: Word position span of the source phrase :type src_phrase_span: tuple(int, int) """ score = hypothesis.raw_score score += translation_option.log_prob # The API of language_model is subject to change; it could accept # a string, a list of words, and/or some other type score += self.language_model.probability_change( hypothesis, translation_option.trg_phrase) score += self.distortion_score(hypothesis, src_phrase_span) score -= self.word_penalty * len(translation_option.trg_phrase) return score
[docs] def distortion_score(self, hypothesis, next_src_phrase_span): if not hypothesis.src_phrase_span: return 0.0 next_src_phrase_start = next_src_phrase_span[0] prev_src_phrase_end = hypothesis.src_phrase_span[1] distortion_distance = next_src_phrase_start - prev_src_phrase_end return abs(distortion_distance) * self.__log_distortion_factor
[docs] def valid_phrases(all_phrases_from, hypothesis): """ Extract phrases from ``all_phrases_from`` that contains words that have not been translated by ``hypothesis`` :param all_phrases_from: Phrases represented by their spans, in the same format as the return value of ``find_all_src_phrases`` :type all_phrases_from: list(list(int)) :type hypothesis: _Hypothesis :return: A list of phrases, represented by their spans, that cover untranslated positions. :rtype: list(tuple(int, int)) """ untranslated_spans = hypothesis.untranslated_spans( len(all_phrases_from)) valid_phrases = [] for available_span in untranslated_spans: start = available_span[0] available_end = available_span[1] while start < available_end: for phrase_end in all_phrases_from[start]: if phrase_end > available_end: # Subsequent elements in all_phrases_from[start] # will also be > available_end, since the # elements are in ascending order break valid_phrases.append((start, phrase_end)) start += 1 return valid_phrases
class _Hypothesis(object): """ Partial solution to a translation. Records the word positions of the phrase being translated, its translation, raw score, and the cost of the untranslated parts of the sentence. When the next phrase is selected to build upon the partial solution, a new _Hypothesis object is created, with a back pointer to the previous hypothesis. To find out which words have been translated so far, look at the ``src_phrase_span`` in the hypothesis chain. Similarly, the translation output can be found by traversing up the chain. """ def __init__(self, raw_score=0.0, src_phrase_span=(), trg_phrase=(), previous=None, future_score=0.0): """ :param raw_score: Likelihood of hypothesis so far. Higher is better. Does not account for untranslated words. :type raw_score: float :param src_phrase_span: Span of word positions covered by the source phrase in this hypothesis expansion. For example, (2, 5) means that the phrase is from the second word up to, but not including the fifth word in the source sentence. :type src_phrase_span: tuple(int) :param trg_phrase: Translation of the source phrase in this hypothesis expansion :type trg_phrase: tuple(str) :param previous: Previous hypothesis before expansion to this one :type previous: _Hypothesis :param future_score: Approximate score for translating the remaining words not covered by this hypothesis. Higher means that the remaining words are easier to translate. :type future_score: float """ self.raw_score = raw_score self.src_phrase_span = src_phrase_span self.trg_phrase = trg_phrase self.previous = previous self.future_score = future_score def score(self): """ Overall score of hypothesis after accounting for local and global features """ return self.raw_score + self.future_score def untranslated_spans(self, sentence_length): """ Starting from each untranslated word, find the longest continuous span of untranslated positions :param sentence_length: Length of source sentence being translated by the hypothesis :type sentence_length: int :rtype: list(tuple(int, int)) """ translated_positions = self.translated_positions() translated_positions.sort() translated_positions.append(sentence_length) # add sentinel position untranslated_spans = [] start = 0 # each untranslated span must end in one of the translated_positions for end in translated_positions: if start < end: untranslated_spans.append((start, end)) start = end + 1 return untranslated_spans def translated_positions(self): """ List of positions in the source sentence of words already translated. The list is not sorted. :rtype: list(int) """ translated_positions = [] current_hypothesis = self while current_hypothesis.previous is not None: translated_span = current_hypothesis.src_phrase_span translated_positions.extend(range(translated_span[0], translated_span[1])) current_hypothesis = current_hypothesis.previous return translated_positions def total_translated_words(self): return len(self.translated_positions()) def translation_so_far(self): translation = [] self.__build_translation(self, translation) return translation def __build_translation(self, hypothesis, output): if hypothesis.previous is None: return self.__build_translation(hypothesis.previous, output) output.extend(hypothesis.trg_phrase) class _Stack(object): """ Collection of _Hypothesis objects """ def __init__(self, max_size=100, beam_threshold=0.0): """ :param beam_threshold: Hypotheses that score less than this factor of the best hypothesis are discarded from the stack. Value must be between 0.0 and 1.0. :type beam_threshold: float """ self.max_size = max_size self.items = [] if beam_threshold == 0.0: self.__log_beam_threshold = float('-inf') else: self.__log_beam_threshold = log(beam_threshold) def push(self, hypothesis): """ Add ``hypothesis`` to the stack. Removes lowest scoring hypothesis if the stack is full. After insertion, hypotheses that score less than ``beam_threshold`` times the score of the best hypothesis are removed. """ self.items.append(hypothesis) self.items.sort(key=lambda h: h.score(), reverse=True) while len(self.items) > self.max_size: self.items.pop() self.threshold_prune() def threshold_prune(self): if not self.items: return # log(score * beam_threshold) = log(score) + log(beam_threshold) threshold = self.items[0].score() + self.__log_beam_threshold for hypothesis in reversed(self.items): if hypothesis.score() < threshold: self.items.pop() else: break def best(self): """ :return: Hypothesis with the highest score in the stack :rtype: _Hypothesis """ if self.items: return self.items[0] return None def __iter__(self): return iter(self.items) def __contains__(self, hypothesis): return hypothesis in self.items def __bool__(self): return len(self.items) != 0 __nonzero__=__bool__