mirror of
https://github.com/moses-smt/mosesdecoder.git
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184 lines
5.5 KiB
C++
184 lines
5.5 KiB
C++
/*
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* File: lexdecom.cpp
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* Author: Felipe Sánchez-Martínez, Universitat d'Alacant <fsanchez@dlsi.ua.es>
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*
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* Created on 2010/01/27
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*/
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#include "lexdecom.h"
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#include "scorer-impl.h"
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#include <iostream>
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#include <fstream>
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PhraseScorer*
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LexicalDecompositionPhraseScorer::create_scorer(const char *argv[], int &argp, bool reverse, const PhraseScorerFactory &ptf)
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{
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if(argv[argp] == NULL)
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usage();
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String lwfile(argv[argp++]);
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return new LexicalDecompositionPhraseScorer(ptf.get_phrase_table(), reverse, lwfile, argv, argp, ptf);
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}
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LexicalDecompositionPhraseScorer::LexicalDecompositionPhraseScorer(PhraseTable &pd, bool reverse,
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const String &weightfile, const char *argv[], int &argp,
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const PhraseScorerFactory &ptf) :
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PhraseScorer(pd, reverse)
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{
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black_box_scorer = AbsoluteDiscountPhraseScorer::create_scorer(argv, argp, reverse, ptf);
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std::ifstream wfile(weightfile.c_str());
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//Code copied from LexicalWeightPhraseScorer; it should be factored !! -- 2010/01/27
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std::cerr<<"Reading lexical weights from '"<<weightfile<<"' ... ";
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while(!wfile.eof()) {
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if(wfile.fail()) {
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std::cerr << "Problem reading file: " << weightfile << std::endl;
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exit(1);
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}
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String src, tgt;
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Score weight;
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wfile >> src >> tgt >> weight;
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Count src_id = PhraseText::index_word(src);
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Count tgt_id = PhraseText::index_word(tgt);
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weight_map_.insert(std::make_pair(std::make_pair(src_id, tgt_id), weight));
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}
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wfile.close();
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std::cerr<<"done."<<std::endl;
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}
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Score
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LexicalDecompositionPhraseScorer::get_weight(const String &s_src, const String &s_tgt) const
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{
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//Code copied from LexicalWeightPhraseScorer; it should be factored !! -- 2010/01/27
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Count src = PhraseText::index_word(s_src);
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Count tgt = PhraseText::index_word(s_tgt);
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return get_weight(src, tgt);
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}
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inline Score
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LexicalDecompositionPhraseScorer::get_weight(Count src, Count tgt) const
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{
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//Code copied from LexicalWeightPhraseScorer; it should be factored !! -- 2010/01/27
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WeightMapType_::const_iterator it = weight_map_.find(std::make_pair(src, tgt));
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if(it == weight_map_.end())
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return 0.00001; // default value copied from Philipp Koehn's scorer
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return it->second;
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}
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void
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LexicalDecompositionPhraseScorer::do_score_phrases()
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{
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//Estimate p(J|I) = p(src_len|tgt_len)
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black_box_scorer->score_phrases();
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std::cerr<<"LexicalDecompositionPhraseScorer::do_score_phrases"<<std::endl;
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std::map<unsigned, std::map<unsigned, Count> > count_srclen_tgtlen;
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std::map<unsigned, Count> total_tgtlen;
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for(PhraseTable::iterator it = phrase_table_.begin(); it != phrase_table_.end(); it++) {
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const PhrasePairInfo &ppair = *it;
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PhraseInfo &src = phrase_table_.get_src_phrase(ppair.get_src());
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PhraseInfo &tgt = phrase_table_.get_tgt_phrase(ppair.get_tgt());
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unsigned src_len = src.get_phrase().size();
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unsigned tgt_len = tgt.get_phrase().size();
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/*//Debug code
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for (unsigned i=0; i<src_len; i++)
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std::cerr<<src.get_phrase().word(i)<<" ";
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std::cerr<<"-> "<<src_len<<" ||| ";
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for (unsigned i=0; i<tgt_len; i++)
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std::cerr<<tgt.get_phrase().word(i)<<" ";
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std::cerr<<"-> "<<tgt_len<<std::endl;
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*/
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count_srclen_tgtlen[src_len][tgt_len]+=ppair.get_count();
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total_tgtlen[tgt_len]+=ppair.get_count();
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}
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std::map<unsigned, std::map<unsigned, Count> >::iterator its;
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std::map<unsigned, Count>::iterator itt;
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for (its=count_srclen_tgtlen.begin(); its!=count_srclen_tgtlen.end(); its++) {
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unsigned src_len=its->first;
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for(itt=its->second.begin(); itt!=its->second.end(); itt++) {
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unsigned tgt_len=itt->first;
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Count cnt=itt->second;
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prob_srclen_tgtlen_[src_len][tgt_len] = static_cast<Score>(cnt)/static_cast<Score>(total_tgtlen[tgt_len]);
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}
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}
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}
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Score
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LexicalDecompositionPhraseScorer::get_noisy_or_combination(Count src_word, PhraseInfo &tgt_phrase)
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{
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Score sc=1.0;
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unsigned tgt_len=tgt_phrase.get_phrase().size();
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for(unsigned i=0; i<tgt_len; i++) {
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Count tgt_word=tgt_phrase.get_phrase()[i];
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sc *= (1.0 - get_weight(src_word, tgt_word));
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}
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return (1.0 - sc);
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}
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Score
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LexicalDecompositionPhraseScorer::do_get_score(const PhraseTable::const_iterator &it)
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{
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/*
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The implementation of this method relies on the asumption that the
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smoothed probabilities produced by AbsoluteDiscountPhraseScorer
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are deficient.
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*/
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PhraseInfo &src_phrase = phrase_table_.get_src_phrase(it->get_src());
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PhraseInfo &tgt_phrase = phrase_table_.get_tgt_phrase(it->get_tgt());
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unsigned src_len=src_phrase.get_phrase().size();
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unsigned tgt_len=tgt_phrase.get_phrase().size();
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Score prod=1.0;
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for(unsigned j=0; j<src_len; j++)
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prod *= get_noisy_or_combination(src_phrase.get_phrase()[j], tgt_phrase);
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Score lambda= static_cast<Score>(black_box_scorer->get_discount()) *
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tgt_phrase.get_distinct() / tgt_phrase.get_count();
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Score ret_value = black_box_scorer->get_score(it) + (lambda * prod * prob_srclen_tgtlen_[src_len][tgt_len]);
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/*
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//Debug code
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for (unsigned i=0; i<src_len; i++)
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std::cerr<<src_phrase.get_phrase().word(i)<<" ";
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std::cerr<<" ||| ";
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for (unsigned i=0; i<tgt_len; i++)
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std::cerr<<tgt_phrase.get_phrase().word(i)<<" ";
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std::cerr<<"==> discount: "<<black_box_scorer->get_discount()<<"; black box score: "<<black_box_scorer->get_score(it)
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<<"; lambda: "<<lambda<<"; prod: "
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<<prod<<"; prob_srclen_tgtlen_["<<src_len<<"]["<<tgt_len<<"]: "<<prob_srclen_tgtlen_[src_len][tgt_len]
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<<"; count: "<<it->get_count()<<"; score: "<<ret_value;
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std::cerr<<std::endl;
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*/
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return ret_value;
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}
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