mirror of
https://github.com/moses-smt/mosesdecoder.git
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182 lines
5.1 KiB
C++
182 lines
5.1 KiB
C++
// $Id$
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/***********************************************************************
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Moses - factored phrase-based language decoder
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Copyright (C) 2006 University of Edinburgh
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This library is free software; you can redistribute it and/or
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modify it under the terms of the GNU Lesser General Public
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License as published by the Free Software Foundation; either
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version 2.1 of the License, or (at your option) any later version.
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This library is distributed in the hope that it will be useful,
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but WITHOUT ANY WARRANTY; without even the implied warranty of
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MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
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Lesser General Public License for more details.
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You should have received a copy of the GNU Lesser General Public
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License along with this library; if not, write to the Free Software
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Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
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***********************************************************************/
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#include "util/check.hh"
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#include <limits>
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#include <iostream>
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#include <fstream>
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#include "SRI.h"
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#include "moses/TypeDef.h"
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#include "moses/Util.h"
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#include "moses/FactorCollection.h"
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#include "moses/Phrase.h"
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#include "moses/StaticData.h"
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// By default, SRILM defines a function called zopen.
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//
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// However, on Mac OS X (and possibly other BSDs),
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// <stdio.h> already defines a zopen function.
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//
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// To resolve this conflict, SRILM checks to see if HAVE_ZOPEN is defined.
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// If it is, SRILM will rename its zopen function as my_zopen.
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//
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// So, before importing any SRILM headers,
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// it is important to define HAVE_ZOPEN if we are on an Apple OS:
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//
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#ifdef __APPLE__
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#define HAVE_ZOPEN
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#endif
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#include "Vocab.h"
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#include "Ngram.h"
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using namespace std;
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namespace Moses
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{
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LanguageModelSRI::LanguageModelSRI(const std::string &line)
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:LanguageModelSingleFactor(line)
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,m_srilmVocab(0)
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,m_srilmModel(0)
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{
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ReadParameters();
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}
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LanguageModelSRI::~LanguageModelSRI()
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{
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delete m_srilmModel;
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delete m_srilmVocab;
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}
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void LanguageModelSRI::Load()
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{
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m_srilmVocab = new ::Vocab();
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m_srilmModel = new Ngram(*m_srilmVocab, m_nGramOrder);
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m_srilmModel->skipOOVs() = false;
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File file( m_filePath.c_str(), "r" );
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m_srilmModel->read(file);
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// LM can be ok, just outputs warnings
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CreateFactors();
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m_unknownId = m_srilmVocab->unkIndex();
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}
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void LanguageModelSRI::CreateFactors()
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{
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// add factors which have srilm id
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FactorCollection &factorCollection = FactorCollection::Instance();
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std::map<size_t, VocabIndex> lmIdMap;
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size_t maxFactorId = 0; // to create lookup vector later on
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VocabString str;
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VocabIter iter(*m_srilmVocab);
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while ( (str = iter.next()) != NULL) {
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VocabIndex lmId = GetLmID(str);
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size_t factorId = factorCollection.AddFactor(Output, m_factorType, str)->GetId();
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lmIdMap[factorId] = lmId;
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maxFactorId = (factorId > maxFactorId) ? factorId : maxFactorId;
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}
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size_t factorId;
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m_sentenceStart = factorCollection.AddFactor(Output, m_factorType, BOS_);
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factorId = m_sentenceStart->GetId();
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lmIdMap[factorId] = GetLmID(BOS_);
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maxFactorId = (factorId > maxFactorId) ? factorId : maxFactorId;
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m_sentenceStartWord[m_factorType] = m_sentenceStart;
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m_sentenceEnd = factorCollection.AddFactor(Output, m_factorType, EOS_);
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factorId = m_sentenceEnd->GetId();
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lmIdMap[factorId] = GetLmID(EOS_);
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maxFactorId = (factorId > maxFactorId) ? factorId : maxFactorId;
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m_sentenceEndWord[m_factorType] = m_sentenceEnd;
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// add to lookup vector in object
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m_lmIdLookup.resize(maxFactorId+1);
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fill(m_lmIdLookup.begin(), m_lmIdLookup.end(), m_unknownId);
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map<size_t, VocabIndex>::iterator iterMap;
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for (iterMap = lmIdMap.begin() ; iterMap != lmIdMap.end() ; ++iterMap) {
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m_lmIdLookup[iterMap->first] = iterMap->second;
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}
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}
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VocabIndex LanguageModelSRI::GetLmID( const std::string &str ) const
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{
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return m_srilmVocab->getIndex( str.c_str(), m_unknownId );
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}
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VocabIndex LanguageModelSRI::GetLmID( const Factor *factor ) const
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{
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size_t factorId = factor->GetId();
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return ( factorId >= m_lmIdLookup.size()) ? m_unknownId : m_lmIdLookup[factorId];
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}
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LMResult LanguageModelSRI::GetValue(VocabIndex wordId, VocabIndex *context) const
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{
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LMResult ret;
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ret.score = FloorScore(TransformLMScore(m_srilmModel->wordProb( wordId, context)));
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ret.unknown = (wordId == m_unknownId);
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return ret;
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}
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LMResult LanguageModelSRI::GetValue(const vector<const Word*> &contextFactor, State* finalState) const
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{
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LMResult ret;
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FactorType factorType = GetFactorType();
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size_t count = contextFactor.size();
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if (count <= 0) {
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if(finalState)
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*finalState = NULL;
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ret.score = 0.0;
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ret.unknown = false;
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return ret;
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}
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// set up context
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VocabIndex ngram[count + 1];
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for (size_t i = 0 ; i < count - 1 ; i++) {
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ngram[i+1] = GetLmID((*contextFactor[count-2-i])[factorType]);
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}
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ngram[count] = Vocab_None;
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CHECK((*contextFactor[count-1])[factorType] != NULL);
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// call sri lm fn
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VocabIndex lmId = GetLmID((*contextFactor[count-1])[factorType]);
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ret = GetValue(lmId, ngram+1);
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if (finalState) {
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ngram[0] = lmId;
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unsigned int dummy;
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*finalState = m_srilmModel->contextID(ngram, dummy);
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}
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return ret;
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}
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}
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