468 lines
20 KiB
Python
468 lines
20 KiB
Python
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# -*- coding: utf-8 -*-
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# Natural Language Toolkit: IBM Model 4
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#
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# Copyright (C) 2001-2018 NLTK Project
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# Author: Tah Wei Hoon <hoon.tw@gmail.com>
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# URL: <http://nltk.org/>
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# For license information, see LICENSE.TXT
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"""
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Translation model that reorders output words based on their type and
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distance from other related words in the output sentence.
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IBM Model 4 improves the distortion model of Model 3, motivated by the
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observation that certain words tend to be re-ordered in a predictable
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way relative to one another. For example, <adjective><noun> in English
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usually has its order flipped as <noun><adjective> in French.
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Model 4 requires words in the source and target vocabularies to be
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categorized into classes. This can be linguistically driven, like parts
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of speech (adjective, nouns, prepositions, etc). Word classes can also
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be obtained by statistical methods. The original IBM Model 4 uses an
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information theoretic approach to group words into 50 classes for each
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vocabulary.
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Terminology:
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Cept:
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A source word with non-zero fertility i.e. aligned to one or more
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target words.
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Tablet:
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The set of target word(s) aligned to a cept.
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Head of cept:
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The first word of the tablet of that cept.
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Center of cept:
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The average position of the words in that cept's tablet. If the
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value is not an integer, the ceiling is taken.
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For example, for a tablet with words in positions 2, 5, 6 in the
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target sentence, the center of the corresponding cept is
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ceil((2 + 5 + 6) / 3) = 5
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Displacement:
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For a head word, defined as (position of head word - position of
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previous cept's center). Can be positive or negative.
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For a non-head word, defined as (position of non-head word -
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position of previous word in the same tablet). Always positive,
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because successive words in a tablet are assumed to appear to the
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right of the previous word.
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In contrast to Model 3 which reorders words in a tablet independently of
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other words, Model 4 distinguishes between three cases.
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(1) Words generated by NULL are distributed uniformly.
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(2) For a head word t, its position is modeled by the probability
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d_head(displacement | word_class_s(s),word_class_t(t)),
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where s is the previous cept, and word_class_s and word_class_t maps
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s and t to a source and target language word class respectively.
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(3) For a non-head word t, its position is modeled by the probability
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d_non_head(displacement | word_class_t(t))
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The EM algorithm used in Model 4 is:
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E step - In the training data, collect counts, weighted by prior
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probabilities.
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(a) count how many times a source language word is translated
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into a target language word
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(b) for a particular word class, count how many times a head
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word is located at a particular displacement from the
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previous cept's center
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(c) for a particular word class, count how many times a
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non-head word is located at a particular displacement from
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the previous target word
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(d) count how many times a source word is aligned to phi number
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of target words
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(e) count how many times NULL is aligned to a target word
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M step - Estimate new probabilities based on the counts from the E step
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Like Model 3, there are too many possible alignments to consider. Thus,
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a hill climbing approach is used to sample good candidates.
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Notations:
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i: Position in the source sentence
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Valid values are 0 (for NULL), 1, 2, ..., length of source sentence
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j: Position in the target sentence
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Valid values are 1, 2, ..., length of target sentence
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l: Number of words in the source sentence, excluding NULL
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m: Number of words in the target sentence
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s: A word in the source language
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t: A word in the target language
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phi: Fertility, the number of target words produced by a source word
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p1: Probability that a target word produced by a source word is
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accompanied by another target word that is aligned to NULL
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p0: 1 - p1
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dj: Displacement, Δj
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References:
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Philipp Koehn. 2010. Statistical Machine Translation.
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Cambridge University Press, New York.
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Peter E Brown, Stephen A. Della Pietra, Vincent J. Della Pietra, and
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Robert L. Mercer. 1993. The Mathematics of Statistical Machine
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Translation: Parameter Estimation. Computational Linguistics, 19 (2),
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263-311.
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"""
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from __future__ import division
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from collections import defaultdict
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from math import factorial
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from nltk.translate import AlignedSent
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from nltk.translate import Alignment
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from nltk.translate import IBMModel
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from nltk.translate import IBMModel3
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from nltk.translate.ibm_model import Counts
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from nltk.translate.ibm_model import longest_target_sentence_length
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import warnings
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class IBMModel4(IBMModel):
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"""
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Translation model that reorders output words based on their type and
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their distance from other related words in the output sentence
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>>> bitext = []
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>>> bitext.append(AlignedSent(['klein', 'ist', 'das', 'haus'], ['the', 'house', 'is', 'small']))
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>>> bitext.append(AlignedSent(['das', 'haus', 'war', 'ja', 'groß'], ['the', 'house', 'was', 'big']))
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>>> bitext.append(AlignedSent(['das', 'buch', 'ist', 'ja', 'klein'], ['the', 'book', 'is', 'small']))
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>>> bitext.append(AlignedSent(['ein', 'haus', 'ist', 'klein'], ['a', 'house', 'is', 'small']))
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>>> bitext.append(AlignedSent(['das', 'haus'], ['the', 'house']))
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>>> bitext.append(AlignedSent(['das', 'buch'], ['the', 'book']))
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>>> bitext.append(AlignedSent(['ein', 'buch'], ['a', 'book']))
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>>> bitext.append(AlignedSent(['ich', 'fasse', 'das', 'buch', 'zusammen'], ['i', 'summarize', 'the', 'book']))
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>>> bitext.append(AlignedSent(['fasse', 'zusammen'], ['summarize']))
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>>> src_classes = {'the': 0, 'a': 0, 'small': 1, 'big': 1, 'house': 2, 'book': 2, 'is': 3, 'was': 3, 'i': 4, 'summarize': 5 }
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>>> trg_classes = {'das': 0, 'ein': 0, 'haus': 1, 'buch': 1, 'klein': 2, 'groß': 2, 'ist': 3, 'war': 3, 'ja': 4, 'ich': 5, 'fasse': 6, 'zusammen': 6 }
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>>> ibm4 = IBMModel4(bitext, 5, src_classes, trg_classes)
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>>> print(round(ibm4.translation_table['buch']['book'], 3))
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1.0
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>>> print(round(ibm4.translation_table['das']['book'], 3))
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0.0
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>>> print(round(ibm4.translation_table['ja'][None], 3))
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1.0
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>>> print(round(ibm4.head_distortion_table[1][0][1], 3))
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1.0
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>>> print(round(ibm4.head_distortion_table[2][0][1], 3))
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0.0
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>>> print(round(ibm4.non_head_distortion_table[3][6], 3))
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0.5
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>>> print(round(ibm4.fertility_table[2]['summarize'], 3))
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1.0
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>>> print(round(ibm4.fertility_table[1]['book'], 3))
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1.0
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>>> print(ibm4.p1)
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0.033...
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>>> test_sentence = bitext[2]
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>>> test_sentence.words
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['das', 'buch', 'ist', 'ja', 'klein']
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>>> test_sentence.mots
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['the', 'book', 'is', 'small']
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>>> test_sentence.alignment
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Alignment([(0, 0), (1, 1), (2, 2), (3, None), (4, 3)])
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"""
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def __init__(self, sentence_aligned_corpus, iterations,
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source_word_classes, target_word_classes,
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probability_tables=None):
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"""
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Train on ``sentence_aligned_corpus`` and create a lexical
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translation model, distortion models, a fertility model, and a
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model for generating NULL-aligned words.
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Translation direction is from ``AlignedSent.mots`` to
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``AlignedSent.words``.
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:param sentence_aligned_corpus: Sentence-aligned parallel corpus
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:type sentence_aligned_corpus: list(AlignedSent)
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:param iterations: Number of iterations to run training algorithm
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:type iterations: int
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:param source_word_classes: Lookup table that maps a source word
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to its word class, the latter represented by an integer id
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:type source_word_classes: dict[str]: int
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:param target_word_classes: Lookup table that maps a target word
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to its word class, the latter represented by an integer id
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:type target_word_classes: dict[str]: int
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:param probability_tables: Optional. Use this to pass in custom
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probability values. If not specified, probabilities will be
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set to a uniform distribution, or some other sensible value.
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If specified, all the following entries must be present:
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``translation_table``, ``alignment_table``,
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``fertility_table``, ``p1``, ``head_distortion_table``,
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``non_head_distortion_table``. See ``IBMModel`` and
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``IBMModel4`` for the type and purpose of these tables.
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:type probability_tables: dict[str]: object
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"""
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super(IBMModel4, self).__init__(sentence_aligned_corpus)
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self.reset_probabilities()
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self.src_classes = source_word_classes
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self.trg_classes = target_word_classes
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if probability_tables is None:
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# Get probabilities from IBM model 3
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ibm3 = IBMModel3(sentence_aligned_corpus, iterations)
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self.translation_table = ibm3.translation_table
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self.alignment_table = ibm3.alignment_table
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self.fertility_table = ibm3.fertility_table
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self.p1 = ibm3.p1
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self.set_uniform_probabilities(sentence_aligned_corpus)
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else:
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# Set user-defined probabilities
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self.translation_table = probability_tables['translation_table']
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self.alignment_table = probability_tables['alignment_table']
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self.fertility_table = probability_tables['fertility_table']
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self.p1 = probability_tables['p1']
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self.head_distortion_table = probability_tables[
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'head_distortion_table']
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self.non_head_distortion_table = probability_tables[
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'non_head_distortion_table']
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for n in range(0, iterations):
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self.train(sentence_aligned_corpus)
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def reset_probabilities(self):
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super(IBMModel4, self).reset_probabilities()
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self.head_distortion_table = defaultdict(
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lambda: defaultdict(lambda: defaultdict(lambda: self.MIN_PROB)))
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"""
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dict[int][int][int]: float. Probability(displacement of head
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word | word class of previous cept,target word class).
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Values accessed as ``distortion_table[dj][src_class][trg_class]``.
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"""
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self.non_head_distortion_table = defaultdict(
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lambda: defaultdict(lambda: self.MIN_PROB))
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"""
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dict[int][int]: float. Probability(displacement of non-head
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word | target word class).
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Values accessed as ``distortion_table[dj][trg_class]``.
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"""
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def set_uniform_probabilities(self, sentence_aligned_corpus):
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"""
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Set distortion probabilities uniformly to
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1 / cardinality of displacement values
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"""
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max_m = longest_target_sentence_length(sentence_aligned_corpus)
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# The maximum displacement is m-1, when a word is in the last
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# position m of the target sentence and the previously placed
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# word is in the first position.
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# Conversely, the minimum displacement is -(m-1).
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# Thus, the displacement range is (m-1) - (-(m-1)). Note that
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# displacement cannot be zero and is not included in the range.
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if max_m <= 1:
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initial_prob = IBMModel.MIN_PROB
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else:
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initial_prob = 1 / (2 * (max_m - 1))
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if initial_prob < IBMModel.MIN_PROB:
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warnings.warn("A target sentence is too long (" + str(max_m) +
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" words). Results may be less accurate.")
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for dj in range(1, max_m):
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self.head_distortion_table[dj] = defaultdict(
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lambda: defaultdict(lambda: initial_prob))
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self.head_distortion_table[-dj] = defaultdict(
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lambda: defaultdict(lambda: initial_prob))
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self.non_head_distortion_table[dj] = defaultdict(
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lambda: initial_prob)
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self.non_head_distortion_table[-dj] = defaultdict(
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lambda: initial_prob)
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def train(self, parallel_corpus):
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counts = Model4Counts()
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for aligned_sentence in parallel_corpus:
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m = len(aligned_sentence.words)
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# Sample the alignment space
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sampled_alignments, best_alignment = self.sample(aligned_sentence)
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# Record the most probable alignment
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aligned_sentence.alignment = Alignment(
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best_alignment.zero_indexed_alignment())
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# E step (a): Compute normalization factors to weigh counts
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total_count = self.prob_of_alignments(sampled_alignments)
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# E step (b): Collect counts
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for alignment_info in sampled_alignments:
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count = self.prob_t_a_given_s(alignment_info)
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normalized_count = count / total_count
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for j in range(1, m + 1):
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counts.update_lexical_translation(
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normalized_count, alignment_info, j)
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counts.update_distortion(
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normalized_count, alignment_info, j,
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self.src_classes, self.trg_classes)
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counts.update_null_generation(normalized_count, alignment_info)
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counts.update_fertility(normalized_count, alignment_info)
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# M step: Update probabilities with maximum likelihood estimates
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# If any probability is less than MIN_PROB, clamp it to MIN_PROB
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existing_alignment_table = self.alignment_table
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self.reset_probabilities()
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self.alignment_table = existing_alignment_table # don't retrain
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self.maximize_lexical_translation_probabilities(counts)
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self.maximize_distortion_probabilities(counts)
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self.maximize_fertility_probabilities(counts)
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self.maximize_null_generation_probabilities(counts)
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def maximize_distortion_probabilities(self, counts):
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head_d_table = self.head_distortion_table
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for dj, src_classes in counts.head_distortion.items():
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for s_cls, trg_classes in src_classes.items():
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for t_cls in trg_classes:
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estimate = (counts.head_distortion[dj][s_cls][t_cls] /
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counts.head_distortion_for_any_dj[s_cls][t_cls])
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head_d_table[dj][s_cls][t_cls] = max(estimate,
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IBMModel.MIN_PROB)
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non_head_d_table = self.non_head_distortion_table
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for dj, trg_classes in counts.non_head_distortion.items():
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for t_cls in trg_classes:
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estimate = (counts.non_head_distortion[dj][t_cls] /
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counts.non_head_distortion_for_any_dj[t_cls])
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non_head_d_table[dj][t_cls] = max(estimate, IBMModel.MIN_PROB)
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||
|
|
|
||
|
|
def prob_t_a_given_s(self, alignment_info):
|
||
|
|
"""
|
||
|
|
Probability of target sentence and an alignment given the
|
||
|
|
source sentence
|
||
|
|
"""
|
||
|
|
return IBMModel4.model4_prob_t_a_given_s(alignment_info, self)
|
||
|
|
|
||
|
|
@staticmethod # exposed for Model 5 to use
|
||
|
|
def model4_prob_t_a_given_s(alignment_info, ibm_model):
|
||
|
|
probability = 1.0
|
||
|
|
MIN_PROB = IBMModel.MIN_PROB
|
||
|
|
|
||
|
|
def null_generation_term():
|
||
|
|
# Binomial distribution: B(m - null_fertility, p1)
|
||
|
|
value = 1.0
|
||
|
|
p1 = ibm_model.p1
|
||
|
|
p0 = 1 - p1
|
||
|
|
null_fertility = alignment_info.fertility_of_i(0)
|
||
|
|
m = len(alignment_info.trg_sentence) - 1
|
||
|
|
value *= (pow(p1, null_fertility) * pow(p0, m - 2 * null_fertility))
|
||
|
|
if value < MIN_PROB:
|
||
|
|
return MIN_PROB
|
||
|
|
|
||
|
|
# Combination: (m - null_fertility) choose null_fertility
|
||
|
|
for i in range(1, null_fertility + 1):
|
||
|
|
value *= (m - null_fertility - i + 1) / i
|
||
|
|
return value
|
||
|
|
|
||
|
|
def fertility_term():
|
||
|
|
value = 1.0
|
||
|
|
src_sentence = alignment_info.src_sentence
|
||
|
|
for i in range(1, len(src_sentence)):
|
||
|
|
fertility = alignment_info.fertility_of_i(i)
|
||
|
|
value *= (factorial(fertility) *
|
||
|
|
ibm_model.fertility_table[fertility][src_sentence[i]])
|
||
|
|
if value < MIN_PROB:
|
||
|
|
return MIN_PROB
|
||
|
|
return value
|
||
|
|
|
||
|
|
def lexical_translation_term(j):
|
||
|
|
t = alignment_info.trg_sentence[j]
|
||
|
|
i = alignment_info.alignment[j]
|
||
|
|
s = alignment_info.src_sentence[i]
|
||
|
|
return ibm_model.translation_table[t][s]
|
||
|
|
|
||
|
|
def distortion_term(j):
|
||
|
|
t = alignment_info.trg_sentence[j]
|
||
|
|
i = alignment_info.alignment[j]
|
||
|
|
if i == 0:
|
||
|
|
# case 1: t is aligned to NULL
|
||
|
|
return 1.0
|
||
|
|
if alignment_info.is_head_word(j):
|
||
|
|
# case 2: t is the first word of a tablet
|
||
|
|
previous_cept = alignment_info.previous_cept(j)
|
||
|
|
src_class = None
|
||
|
|
if previous_cept is not None:
|
||
|
|
previous_s = alignment_info.src_sentence[previous_cept]
|
||
|
|
src_class = ibm_model.src_classes[previous_s]
|
||
|
|
trg_class = ibm_model.trg_classes[t]
|
||
|
|
dj = j - alignment_info.center_of_cept(previous_cept)
|
||
|
|
return ibm_model.head_distortion_table[dj][src_class][trg_class]
|
||
|
|
|
||
|
|
# case 3: t is a subsequent word of a tablet
|
||
|
|
previous_position = alignment_info.previous_in_tablet(j)
|
||
|
|
trg_class = ibm_model.trg_classes[t]
|
||
|
|
dj = j - previous_position
|
||
|
|
return ibm_model.non_head_distortion_table[dj][trg_class]
|
||
|
|
# end nested functions
|
||
|
|
|
||
|
|
# Abort computation whenever probability falls below MIN_PROB at
|
||
|
|
# any point, since MIN_PROB can be considered as zero
|
||
|
|
probability *= null_generation_term()
|
||
|
|
if probability < MIN_PROB:
|
||
|
|
return MIN_PROB
|
||
|
|
|
||
|
|
probability *= fertility_term()
|
||
|
|
if probability < MIN_PROB:
|
||
|
|
return MIN_PROB
|
||
|
|
|
||
|
|
for j in range(1, len(alignment_info.trg_sentence)):
|
||
|
|
probability *= lexical_translation_term(j)
|
||
|
|
if probability < MIN_PROB:
|
||
|
|
return MIN_PROB
|
||
|
|
|
||
|
|
probability *= distortion_term(j)
|
||
|
|
if probability < MIN_PROB:
|
||
|
|
return MIN_PROB
|
||
|
|
|
||
|
|
return probability
|
||
|
|
|
||
|
|
|
||
|
|
class Model4Counts(Counts):
|
||
|
|
"""
|
||
|
|
Data object to store counts of various parameters during training.
|
||
|
|
Includes counts for distortion.
|
||
|
|
"""
|
||
|
|
def __init__(self):
|
||
|
|
super(Model4Counts, self).__init__()
|
||
|
|
self.head_distortion = defaultdict(
|
||
|
|
lambda: defaultdict(lambda: defaultdict(lambda: 0.0)))
|
||
|
|
self.head_distortion_for_any_dj = defaultdict(
|
||
|
|
lambda: defaultdict(lambda: 0.0))
|
||
|
|
self.non_head_distortion = defaultdict(
|
||
|
|
lambda: defaultdict(lambda: 0.0))
|
||
|
|
self.non_head_distortion_for_any_dj = defaultdict(lambda: 0.0)
|
||
|
|
|
||
|
|
def update_distortion(self, count, alignment_info, j,
|
||
|
|
src_classes, trg_classes):
|
||
|
|
i = alignment_info.alignment[j]
|
||
|
|
t = alignment_info.trg_sentence[j]
|
||
|
|
if i == 0:
|
||
|
|
# case 1: t is aligned to NULL
|
||
|
|
pass
|
||
|
|
elif alignment_info.is_head_word(j):
|
||
|
|
# case 2: t is the first word of a tablet
|
||
|
|
previous_cept = alignment_info.previous_cept(j)
|
||
|
|
if previous_cept is not None:
|
||
|
|
previous_src_word = alignment_info.src_sentence[previous_cept]
|
||
|
|
src_class = src_classes[previous_src_word]
|
||
|
|
else:
|
||
|
|
src_class = None
|
||
|
|
trg_class = trg_classes[t]
|
||
|
|
dj = j - alignment_info.center_of_cept(previous_cept)
|
||
|
|
self.head_distortion[dj][src_class][trg_class] += count
|
||
|
|
self.head_distortion_for_any_dj[src_class][trg_class] += count
|
||
|
|
else:
|
||
|
|
# case 3: t is a subsequent word of a tablet
|
||
|
|
previous_j = alignment_info.previous_in_tablet(j)
|
||
|
|
trg_class = trg_classes[t]
|
||
|
|
dj = j - previous_j
|
||
|
|
self.non_head_distortion[dj][trg_class] += count
|
||
|
|
self.non_head_distortion_for_any_dj[trg_class] += count
|