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/***********************************************************************
Moses - factored phrase-based language decoder
Copyright (C) 2013- University of Edinburgh
This library is free software; you can redistribute it and/or
modify it under the terms of the GNU Lesser General Public
License as published by the Free Software Foundation; either
version 2.1 of the License, or (at your option) any later version.
This library is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public
License along with this library; if not, write to the Free Software
Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
***********************************************************************/
#include <boost/lexical_cast.hpp>
#include <boost/unordered_set.hpp>
#include "util/exception.hh"
#include "util/tokenize_piece.hh"
#include "moses/TranslationModel/PhraseDictionaryInterpolated.h"
using namespace std;
namespace Moses
{
PhraseDictionaryInterpolated::PhraseDictionaryInterpolated
(size_t numScoreComponent,size_t numInputScores,const PhraseDictionaryFeature* feature):
PhraseDictionary(numScoreComponent,feature),
m_targetPhrases(NULL),
m_languageModels(NULL) {}
bool PhraseDictionaryInterpolated::Load(
const std::vector<FactorType> &input
, const std::vector<FactorType> &output
, const std::vector<std::string>& config
, const std::vector<float> &weightT
, size_t tableLimit
, const LMList &languageModels
, float weightWP)
{
m_languageModels = &languageModels;
m_weightT = weightT;
m_tableLimit = tableLimit;
m_weightWP = weightWP;
//The config should be as follows:
//0-3: type factor factor num-components (as usual)
//4: combination mode (e.g. naive)
//5-(length-2): List of phrase-table files
//length-1: Weight string, in the same format as used for tmcombine
UTIL_THROW_IF(config.size() < 7, util::Exception, "Missing fields from phrase table configuration: expected at least 7");
UTIL_THROW_IF(config[4] != "naive", util::Exception, "Unsupported combination mode: '" << config[4] << "'");
// Create the dictionaries
for (size_t i = 5; i < config.size()-1; ++i) {
m_dictionaries.push_back(DictionaryHandle(new PhraseDictionaryTreeAdaptor(
GetFeature()->GetNumScoreComponents(),
GetFeature()->GetNumInputScores(),
GetFeature())));
bool ret = m_dictionaries.back()->Load(
input,
output,
config[i],
weightT,
0,
languageModels,
weightWP);
if (!ret) return ret;
}
//Parse the weight strings
for (util::TokenIter<util::SingleCharacter, false> featureWeights(config.back(), util::SingleCharacter(';')); featureWeights; ++featureWeights) {
m_weights.push_back(vector<float>());
float sum = 0;
for (util::TokenIter<util::SingleCharacter, false> tableWeights(*featureWeights, util::SingleCharacter(',')); tableWeights; ++tableWeights) {
const float weight = boost::lexical_cast<float>(*tableWeights);
m_weights.back().push_back(weight);
sum += weight;
}
UTIL_THROW_IF(m_weights.back().size() != m_dictionaries.size(), util::Exception,
"Number of weights (" << m_weights.back().size() <<
") does not match number of dictionaries to combine (" << m_dictionaries.size() << ")");
UTIL_THROW_IF(abs(sum - 1) > 0.01, util::Exception, "Weights not normalised");
}
//check number of weight sets. Make sure there is a weight for every score component
//except for the last - which is assumed to be the phrase penalty.
UTIL_THROW_IF(m_weights.size() != 1 && m_weights.size() != GetFeature()->GetNumScoreComponents()-1, util::Exception, "Unexpected number of weight sets");
//if 1 weight set, then repeat
if (m_weights.size() == 1) {
while(m_weights.size() < GetFeature()->GetNumScoreComponents()-1) {
m_weights.push_back(m_weights[0]);
}
}
return true;
}
void PhraseDictionaryInterpolated::InitializeForInput(ttasksptr const& ttask)
{
for (size_t i = 0; i < m_dictionaries.size(); ++i) {
m_dictionaries[i]->InitializeForInput(ttask);
}
}
typedef
boost::unordered_set<TargetPhrase*,PhrasePtrHasher,PhrasePtrComparator> PhraseSet;
TargetPhraseCollection::shared_ptr
PhraseDictionaryInterpolated::GetTargetPhraseCollection(const Phrase& src) const
{
delete m_targetPhrases;
m_targetPhrases = new TargetPhraseCollection();
PhraseSet allPhrases;
vector<PhraseSet> phrasesByTable(m_dictionaries.size());
for (size_t i = 0; i < m_dictionaries.size(); ++i) {
TargetPhraseCollection::shared_ptr phrases = m_dictionaries[i]->GetTargetPhraseCollection(src);
if (phrases) {
for (TargetPhraseCollection::const_iterator j = phrases->begin();
j != phrases->end(); ++j) {
allPhrases.insert(*j);
phrasesByTable[i].insert(*j);
}
}
}
ScoreComponentCollection sparseVector;
for (PhraseSet::const_iterator i = allPhrases.begin(); i != allPhrases.end(); ++i) {
TargetPhrase* combinedPhrase = new TargetPhrase((Phrase)**i);
//combinedPhrase->ResetScore();
//cerr << *combinedPhrase << " " << combinedPhrase->GetScoreBreakdown() << endl;
combinedPhrase->SetSourcePhrase((*i)->GetSourcePhrase());
combinedPhrase->SetAlignTerm(&((*i)->GetAlignTerm()));
combinedPhrase->SetAlignNonTerm(&((*i)->GetAlignTerm()));
Scores combinedScores(GetFeature()->GetNumScoreComponents());
for (size_t j = 0; j < phrasesByTable.size(); ++j) {
PhraseSet::const_iterator tablePhrase = phrasesByTable[j].find(combinedPhrase);
if (tablePhrase != phrasesByTable[j].end()) {
Scores tableScores = (*tablePhrase)->GetScoreBreakdown()
.GetScoresForProducer(GetFeature());
//cerr << "Scores from " << j << " table: ";
for (size_t k = 0; k < tableScores.size()-1; ++k) {
//cerr << tableScores[k] << "(" << exp(tableScores[k]) << ") ";
combinedScores[k] += m_weights[k][j] * exp(tableScores[k]);
//cerr << m_weights[k][j] * exp(tableScores[k]) << " ";
}
//cerr << endl;
}
}
//map back to log space
//cerr << "Combined ";
for (size_t k = 0; k < combinedScores.size()-1; ++k) {
//cerr << combinedScores[k] << " ";
combinedScores[k] = log(combinedScores[k]);
//cerr << combinedScores[k] << " ";
}
//cerr << endl;
combinedScores.back() = 1; //assume last is penalty
combinedPhrase->SetScore(
GetFeature(),
combinedScores,
sparseVector,
m_weightT,
m_weightWP,
*m_languageModels);
//cerr << *combinedPhrase << " " << combinedPhrase->GetScoreBreakdown() << endl;
m_targetPhrases->Add(combinedPhrase);
}
m_targetPhrases->Prune(true,m_tableLimit);
return m_targetPhrases;
}
}
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