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// $Id$
/***********************************************************************
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
***********************************************************************/
#ifndef moses_LanguageModelImplementation_h
#define moses_LanguageModelImplementation_h
#include <string>
#include <vector>
#include "Factor.h"
#include "TypeDef.h"
#include "Util.h"
#include "FeatureFunction.h"
#include "Word.h"
#include "LM/Base.h"
#include <boost/shared_ptr.hpp>
namespace Moses
{
class LanguageModel;
class FactorCollection;
class Factor;
class Phrase;
struct LMResult {
// log probability
float score;
// Is the word unknown?
bool unknown;
};
//! Abstract base class which represent a language model on a contiguous phrase
class LanguageModelImplementation
{
// default constructor is ok
void ShiftOrPush(std::vector<const Word*> &contextFactor, const Word &word) const;
protected:
std::string m_filePath; //! for debugging purposes
size_t m_nGramOrder; //! max n-gram length contained in this LM
Word m_sentenceStartArray, m_sentenceEndArray; //! Contains factors which represents the beging and end words for this LM.
//! Usually <s> and </s>
public:
virtual ~LanguageModelImplementation() {}
//! Single or multi-factor
virtual LMType GetLMType() const = 0;
/* whether this LM can be used on a particular phrase.
* Should return false if phrase size = 0 or factor types required don't exists
*/
virtual bool Useable(const Phrase &phrase) const = 0;
/* get score of n-gram. n-gram should not be bigger than m_nGramOrder
* Specific implementation can return State and len data to be used in hypothesis pruning
* \param contextFactor n-gram to be scored
* \param state LM state. Input and output. state must be initialized. If state isn't initialized, you want GetValueWithoutState.
*/
virtual LMResult GetValueGivenState(const std::vector<const Word*> &contextFactor, FFState &state) const;
// Like GetValueGivenState but state may not be initialized (however it is non-NULL).
// For example, state just came from NewState(NULL).
virtual LMResult GetValueForgotState(const std::vector<const Word*> &contextFactor, FFState &outState) const = 0;
//! get State for a particular n-gram. We don't care what the score is.
// This is here so models can implement a shortcut to GetValueAndState.
virtual void GetState(const std::vector<const Word*> &contextFactor, FFState &outState) const;
virtual const FFState *GetNullContextState() const = 0;
virtual const FFState *GetBeginSentenceState() const = 0;
virtual FFState *NewState(const FFState *from = NULL) const = 0;
void CalcScore(const Phrase &phrase, float &fullScore, float &ngramScore, size_t &oovCount) const;
FFState *Evaluate(const Hypothesis &hypo, const FFState *ps, ScoreComponentCollection *out, const LanguageModel *feature) const;
FFState* EvaluateChart(const ChartHypothesis& cur_hypo, int featureID, ScoreComponentCollection* accumulator, const LanguageModel *feature) const;
void updateChartScore(float *prefixScore, float *finalScore, float score, size_t wordPos) const;
//! max n-gram order of LM
size_t GetNGramOrder() const {
return m_nGramOrder;
}
//! Contains factors which represents the beging and end words for this LM. Usually <s> and </s>
const Word &GetSentenceStartArray() const {
return m_sentenceStartArray;
}
const Word &GetSentenceEndArray() const {
return m_sentenceEndArray;
}
std::string GetScoreProducerWeightShortName(unsigned) const {
return "lm";
}
//! overrideable funtions for IRST LM to cleanup. Maybe something to do with on demand/cache loading/unloading
virtual void InitializeBeforeSentenceProcessing() {};
virtual void CleanUpAfterSentenceProcessing() {};
};
class LMRefCount : public LanguageModel {
public:
LMRefCount(LanguageModelImplementation *impl) : m_impl(impl) {}
LanguageModel *Duplicate() const {
return new LMRefCount(*this);
}
void InitializeBeforeSentenceProcessing() {
m_impl->InitializeBeforeSentenceProcessing();
}
void CleanUpAfterSentenceProcessing() {
m_impl->CleanUpAfterSentenceProcessing();
}
const FFState* EmptyHypothesisState(const InputType &/*input*/) const {
return m_impl->NewState(m_impl->GetBeginSentenceState());
}
bool Useable(const Phrase &phrase) const {
return m_impl->Useable(phrase);
}
void CalcScore(const Phrase &phrase, float &fullScore, float &ngramScore, size_t &oovCount) const {
return m_impl->CalcScore(phrase, fullScore, ngramScore, oovCount);
}
FFState* Evaluate(const Hypothesis& cur_hypo, const FFState* prev_state, ScoreComponentCollection* accumulator) const {
return m_impl->Evaluate(cur_hypo, prev_state, accumulator, this);
}
FFState* EvaluateChart(const ChartHypothesis& cur_hypo, int featureID, ScoreComponentCollection* accumulator) const {
return m_impl->EvaluateChart(cur_hypo, featureID, accumulator, this);
}
private:
LMRefCount(const LMRefCount ©_from) : m_impl(copy_from.m_impl) {}
boost::shared_ptr<LanguageModelImplementation> m_impl;
};
}
#endif
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