Source code for nltk.translate.ibm2

# -*- coding: utf-8 -*-
# Natural Language Toolkit: IBM Model 2
#
# Copyright (C) 2001-2013 NLTK Project
# Authors: Chin Yee Lee, Hengfeng Li, Ruxin Hou, Calvin Tanujaya Lim
# URL: <http://nltk.org/>
# For license information, see LICENSE.TXT

"""
Lexical translation model that considers word order.

IBM Model 2 improves on Model 1 by accounting for word order.
An alignment probability is introduced, a(i | j,l,m), which predicts
a source word position, given its aligned target word's position.

The EM algorithm used in Model 2 is:
E step - In the training data, collect counts, weighted by prior
         probabilities.
         (a) count how many times a source language word is translated
             into a target language word
         (b) count how many times a particular position in the source
             sentence is aligned to a particular position in the target
             sentence

M step - Estimate new probabilities based on the counts from the E step


Notations:
i: Position in the source sentence
    Valid values are 0 (for NULL), 1, 2, ..., length of source sentence
j: Position in the target sentence
    Valid values are 1, 2, ..., length of target sentence
l: Number of words in the source sentence, excluding NULL
m: Number of words in the target sentence
s: A word in the source language
t: A word in the target language


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

Peter E Brown, Stephen A. Della Pietra, Vincent J. Della Pietra, and
Robert L. Mercer. 1993. The Mathematics of Statistical Machine
Translation: Parameter Estimation. Computational Linguistics, 19 (2),
263-311.
"""

from __future__ import division
from collections import defaultdict
from nltk.translate import AlignedSent
from nltk.translate import Alignment
from nltk.translate import IBMModel
from nltk.translate import IBMModel1
from nltk.translate.ibm_model import Counts
import warnings


[docs]class IBMModel2(IBMModel): """ Lexical translation model that considers word order >>> bitext = [] >>> bitext.append(AlignedSent(['klein', 'ist', 'das', 'haus'], ['the', 'house', 'is', 'small'])) >>> bitext.append(AlignedSent(['das', 'haus', 'ist', 'ja', 'groƟ'], ['the', 'house', 'is', 'big'])) >>> bitext.append(AlignedSent(['das', 'buch', 'ist', 'ja', 'klein'], ['the', 'book', 'is', 'small'])) >>> bitext.append(AlignedSent(['das', 'haus'], ['the', 'house'])) >>> bitext.append(AlignedSent(['das', 'buch'], ['the', 'book'])) >>> bitext.append(AlignedSent(['ein', 'buch'], ['a', 'book'])) >>> ibm2 = IBMModel2(bitext, 5) >>> print(round(ibm2.translation_table['buch']['book'], 3)) 1.0 >>> print(round(ibm2.translation_table['das']['book'], 3)) 0.0 >>> print(round(ibm2.translation_table['buch'][None], 3)) 0.0 >>> print(round(ibm2.translation_table['ja'][None], 3)) 0.0 >>> print(ibm2.alignment_table[1][1][2][2]) 0.938... >>> print(round(ibm2.alignment_table[1][2][2][2], 3)) 0.0 >>> print(round(ibm2.alignment_table[2][2][4][5], 3)) 1.0 >>> test_sentence = bitext[2] >>> test_sentence.words ['das', 'buch', 'ist', 'ja', 'klein'] >>> test_sentence.mots ['the', 'book', 'is', 'small'] >>> test_sentence.alignment Alignment([(0, 0), (1, 1), (2, 2), (3, 2), (4, 3)]) """ def __init__(self, sentence_aligned_corpus, iterations, probability_tables=None): """ Train on ``sentence_aligned_corpus`` and create a lexical translation model and an alignment model. Translation direction is from ``AlignedSent.mots`` to ``AlignedSent.words``. :param sentence_aligned_corpus: Sentence-aligned parallel corpus :type sentence_aligned_corpus: list(AlignedSent) :param iterations: Number of iterations to run training algorithm :type iterations: int :param probability_tables: Optional. Use this to pass in custom probability values. If not specified, probabilities will be set to a uniform distribution, or some other sensible value. If specified, all the following entries must be present: ``translation_table``, ``alignment_table``. See ``IBMModel`` for the type and purpose of these tables. :type probability_tables: dict[str]: object """ super(IBMModel2, self).__init__(sentence_aligned_corpus) if probability_tables is None: # Get translation probabilities from IBM Model 1 # Run more iterations of training for Model 1, since it is # faster than Model 2 ibm1 = IBMModel1(sentence_aligned_corpus, 2 * iterations) self.translation_table = ibm1.translation_table self.set_uniform_probabilities(sentence_aligned_corpus) else: # Set user-defined probabilities self.translation_table = probability_tables['translation_table'] self.alignment_table = probability_tables['alignment_table'] for n in range(0, iterations): self.train(sentence_aligned_corpus) self.align_all(sentence_aligned_corpus)
[docs] def set_uniform_probabilities(self, sentence_aligned_corpus): # a(i | j,l,m) = 1 / (l+1) for all i, j, l, m l_m_combinations = set() for aligned_sentence in sentence_aligned_corpus: l = len(aligned_sentence.mots) m = len(aligned_sentence.words) if (l, m) not in l_m_combinations: l_m_combinations.add((l, m)) initial_prob = 1 / (l + 1) if initial_prob < IBMModel.MIN_PROB: warnings.warn("A source sentence is too long (" + str(l) + " words). Results may be less accurate.") for i in range(0, l + 1): for j in range(1, m + 1): self.alignment_table[i][j][l][m] = initial_prob
[docs] def train(self, parallel_corpus): counts = Model2Counts() for aligned_sentence in parallel_corpus: src_sentence = [None] + aligned_sentence.mots trg_sentence = ['UNUSED'] + aligned_sentence.words # 1-indexed l = len(aligned_sentence.mots) m = len(aligned_sentence.words) # E step (a): Compute normalization factors to weigh counts total_count = self.prob_all_alignments(src_sentence, trg_sentence) # E step (b): Collect counts for j in range(1, m + 1): t = trg_sentence[j] for i in range(0, l + 1): s = src_sentence[i] count = self.prob_alignment_point( i, j, src_sentence, trg_sentence) normalized_count = count / total_count[t] counts.update_lexical_translation(normalized_count, s, t) counts.update_alignment(normalized_count, i, j, l, m) # M step: Update probabilities with maximum likelihood estimates self.maximize_lexical_translation_probabilities(counts) self.maximize_alignment_probabilities(counts)
[docs] def maximize_alignment_probabilities(self, counts): MIN_PROB = IBMModel.MIN_PROB for i, j_s in counts.alignment.items(): for j, src_sentence_lengths in j_s.items(): for l, trg_sentence_lengths in src_sentence_lengths.items(): for m in trg_sentence_lengths: estimate = (counts.alignment[i][j][l][m] / counts.alignment_for_any_i[j][l][m]) self.alignment_table[i][j][l][m] = max(estimate, MIN_PROB)
[docs] def prob_all_alignments(self, src_sentence, trg_sentence): """ Computes the probability of all possible word alignments, expressed as a marginal distribution over target words t Each entry in the return value represents the contribution to the total alignment probability by the target word t. To obtain probability(alignment | src_sentence, trg_sentence), simply sum the entries in the return value. :return: Probability of t for all s in ``src_sentence`` :rtype: dict(str): float """ alignment_prob_for_t = defaultdict(lambda: 0.0) for j in range(1, len(trg_sentence)): t = trg_sentence[j] for i in range(0, len(src_sentence)): alignment_prob_for_t[t] += self.prob_alignment_point( i, j, src_sentence, trg_sentence) return alignment_prob_for_t
[docs] def prob_alignment_point(self, i, j, src_sentence, trg_sentence): """ Probability that position j in ``trg_sentence`` is aligned to position i in the ``src_sentence`` """ l = len(src_sentence) - 1 m = len(trg_sentence) - 1 s = src_sentence[i] t = trg_sentence[j] return self.translation_table[t][s] * self.alignment_table[i][j][l][m]
[docs] def prob_t_a_given_s(self, alignment_info): """ Probability of target sentence and an alignment given the source sentence """ prob = 1.0 l = len(alignment_info.src_sentence) - 1 m = len(alignment_info.trg_sentence) - 1 for j, i in enumerate(alignment_info.alignment): if j == 0: continue # skip the dummy zeroeth element trg_word = alignment_info.trg_sentence[j] src_word = alignment_info.src_sentence[i] prob *= (self.translation_table[trg_word][src_word] * self.alignment_table[i][j][l][m]) return max(prob, IBMModel.MIN_PROB)
[docs] def align_all(self, parallel_corpus): for sentence_pair in parallel_corpus: self.align(sentence_pair)
[docs] def align(self, sentence_pair): """ Determines the best word alignment for one sentence pair from the corpus that the model was trained on. The best alignment will be set in ``sentence_pair`` when the method returns. In contrast with the internal implementation of IBM models, the word indices in the ``Alignment`` are zero- indexed, not one-indexed. :param sentence_pair: A sentence in the source language and its counterpart sentence in the target language :type sentence_pair: AlignedSent """ best_alignment = [] l = len(sentence_pair.mots) m = len(sentence_pair.words) for j, trg_word in enumerate(sentence_pair.words): # Initialize trg_word to align with the NULL token best_prob = (self.translation_table[trg_word][None] * self.alignment_table[0][j + 1][l][m]) best_prob = max(best_prob, IBMModel.MIN_PROB) best_alignment_point = None for i, src_word in enumerate(sentence_pair.mots): align_prob = (self.translation_table[trg_word][src_word] * self.alignment_table[i + 1][j + 1][l][m]) if align_prob >= best_prob: best_prob = align_prob best_alignment_point = i best_alignment.append((j, best_alignment_point)) sentence_pair.alignment = Alignment(best_alignment)
[docs]class Model2Counts(Counts): """ Data object to store counts of various parameters during training. Includes counts for alignment. """ def __init__(self): super(Model2Counts, self).__init__() self.alignment = defaultdict( lambda: defaultdict(lambda: defaultdict(lambda: defaultdict( lambda: 0.0)))) self.alignment_for_any_i = defaultdict( lambda: defaultdict(lambda: defaultdict(lambda: 0.0)))
[docs] def update_lexical_translation(self, count, s, t): self.t_given_s[t][s] += count self.any_t_given_s[s] += count
[docs] def update_alignment(self, count, i, j, l, m): self.alignment[i][j][l][m] += count self.alignment_for_any_i[j][l][m] += count