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// $Id$
// vim:tabstop=2
/***********************************************************************
Moses - factored phrase-based language decoder
Copyright (C) 2006 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 "TranslationOption.h"
#include "WordsBitmap.h"
#include "PhraseDictionaryMemory.h"
#include "GenerationDictionary.h"
#include "LMList.h"
#include "LexicalReordering.h"
#include "StaticData.h"
#include "InputType.h"
using namespace std;
namespace Moses
{
//TODO this should be a factory function!
TranslationOption::TranslationOption(const WordsRange &wordsRange
, const TargetPhrase &targetPhrase
, const InputType &inputType)
: m_targetPhrase(targetPhrase)
, m_sourceWordsRange(wordsRange)
{
// set score
m_scoreBreakdown.PlusEquals(targetPhrase.GetScoreBreakdown());
if (inputType.GetType() == SentenceInput)
{
Phrase phrase = inputType.GetSubString(wordsRange);
m_sourcePhrase = new Phrase(phrase);
}
else
{ // TODO lex reordering with confusion network
m_sourcePhrase = new Phrase(targetPhrase.GetSourcePhrase());
}
}
//TODO this should be a factory function!
TranslationOption::TranslationOption(const WordsRange &wordsRange
, const TargetPhrase &targetPhrase
, const InputType &inputType
, const UnknownWordPenaltyProducer* up)
: m_targetPhrase(targetPhrase)
, m_sourceWordsRange (wordsRange)
, m_futureScore(0)
{
if (up) {
const ScoreProducer *scoreProducer = (const ScoreProducer *)up; // not sure why none of the c++ cast works
vector<float> score(1);
score[0] = FloorScore(-numeric_limits<float>::infinity());
m_scoreBreakdown.Assign(scoreProducer, score);
}
if (inputType.GetType() == SentenceInput)
{
Phrase phrase = inputType.GetSubString(wordsRange);
m_sourcePhrase = new Phrase(phrase);
}
else
{ // TODO lex reordering with confusion network
m_sourcePhrase = new Phrase(targetPhrase.GetSourcePhrase());
//the target phrase from a confusion network/lattice has input scores that we want to keep
m_scoreBreakdown.PlusEquals(targetPhrase.GetScoreBreakdown());
}
}
TranslationOption::TranslationOption(const TranslationOption ©)
: m_targetPhrase(copy.m_targetPhrase)
//, m_sourcePhrase(new Phrase(*copy.m_sourcePhrase)) // TODO use when confusion network trans opt for confusion net properly implemented
, m_sourcePhrase( (copy.m_sourcePhrase == NULL) ? new Phrase(ARRAY_SIZE_INCR) : new Phrase(*copy.m_sourcePhrase))
, m_sourceWordsRange(copy.m_sourceWordsRange)
, m_futureScore(copy.m_futureScore)
, m_scoreBreakdown(copy.m_scoreBreakdown)
, m_cachedScores(copy.m_cachedScores)
{}
TranslationOption::TranslationOption(const TranslationOption ©, const WordsRange &sourceWordsRange)
: m_targetPhrase(copy.m_targetPhrase)
//, m_sourcePhrase(new Phrase(*copy.m_sourcePhrase)) // TODO use when confusion network trans opt for confusion net properly implemented
, m_sourcePhrase( (copy.m_sourcePhrase == NULL) ? new Phrase(ARRAY_SIZE_INCR) : new Phrase(*copy.m_sourcePhrase))
, m_sourceWordsRange(sourceWordsRange)
, m_futureScore(copy.m_futureScore)
, m_scoreBreakdown(copy.m_scoreBreakdown)
, m_cachedScores(copy.m_cachedScores)
{}
void TranslationOption::MergeNewFeatures(const Phrase& phrase, const ScoreComponentCollection& score, const std::vector<FactorType>& featuresToAdd)
{
CHECK(phrase.GetSize() == m_targetPhrase.GetSize());
if (featuresToAdd.size() == 1) {
m_targetPhrase.MergeFactors(phrase, featuresToAdd[0]);
} else if (featuresToAdd.empty()) {
/* features already there, just update score */
} else {
m_targetPhrase.MergeFactors(phrase, featuresToAdd);
}
m_scoreBreakdown.PlusEquals(score);
}
bool TranslationOption::IsCompatible(const Phrase& phrase, const std::vector<FactorType>& featuresToCheck) const
{
if (featuresToCheck.size() == 1) {
return m_targetPhrase.IsCompatible(phrase, featuresToCheck[0]);
} else if (featuresToCheck.empty()) {
return true;
/* features already there, just update score */
} else {
return m_targetPhrase.IsCompatible(phrase, featuresToCheck);
}
}
bool TranslationOption::Overlap(const Hypothesis &hypothesis) const
{
const WordsBitmap &bitmap = hypothesis.GetWordsBitmap();
return bitmap.Overlap(GetSourceWordsRange());
}
void TranslationOption::CalcScore(const TranslationSystem* system)
{
// LM scores
float ngramScore = 0;
float retFullScore = 0;
float oovScore = 0;
const LMList &allLM = system->GetLanguageModels();
allLM.CalcScore(GetTargetPhrase(), retFullScore, ngramScore, oovScore, &m_scoreBreakdown);
size_t phraseSize = GetTargetPhrase().GetSize();
// future score
m_futureScore = retFullScore - ngramScore + oovScore
+ m_scoreBreakdown.InnerProduct(StaticData::Instance().GetAllWeights()) - phraseSize *
system->GetWeightWordPenalty();
}
TO_STRING_BODY(TranslationOption);
// friend
ostream& operator<<(ostream& out, const TranslationOption& possibleTranslation)
{
out << possibleTranslation.GetTargetPhrase()
<< " c=" << possibleTranslation.GetFutureScore()
<< " [" << possibleTranslation.GetSourceWordsRange() << "]"
<< possibleTranslation.GetScoreBreakdown();
return out;
}
void TranslationOption::CacheScores(const ScoreProducer &producer, const Scores &score)
{
m_cachedScores[&producer] = new Scores(score);
}
}
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