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// -*- mode: c++; indent-tabs-mode: nil; tab-width:2 -*-
// $Id$
/***********************************************************************
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_LanguageModel_h
#define moses_LanguageModel_h
#include <string>
#include <cstddef>
#include "moses/FF/StatefulFeatureFunction.h"
namespace Moses
{
namespace Incremental
{
class Manager;
}
class FactorCollection;
class Factor;
class Phrase;
//! Abstract base class which represent a language model on a contiguous phrase
class LanguageModel : public StatefulFeatureFunction
{
protected:
LanguageModel(const std::string &line);
bool m_enableOOVFeature;
public:
static const LanguageModel &GetFirstLM();
virtual ~LanguageModel();
bool OOVFeatureEnabled() const {
return m_enableOOVFeature;
}
virtual void SetParameter(const std::string& key, const std::string& value);
virtual const FFState* EmptyHypothesisState(const InputType &input) const = 0;
/* calc total unweighted LM score of this phrase and return score via arguments.
* Return scores should always be in natural log, regardless of representation with LM implementation.
* Uses GetValue() of inherited class.
* \param fullScore scores of all unigram, bigram... of contiguous n-gram of the phrase
* \param ngramScore score of only n-gram of order m_nGramOrder
* \param oovCount number of LM OOVs
*/
virtual void CalcScore(const Phrase &phrase, float &fullScore, float &ngramScore, std::size_t &oovCount) const = 0;
virtual void CalcScoreFromCache(const Phrase &phrase, float &fullScore, float &ngramScore, std::size_t &oovCount) const {
}
virtual void IssueRequestsFor(Hypothesis& hypo,
const FFState* input_state) {
}
virtual void sync() {
}
virtual void SetFFStateIdx(int state_idx) {
}
// KenLM only (others throw an exception): call incremental search with the model and mapping.
virtual void IncrementalCallback(Incremental::Manager &manager) const;
virtual void ReportHistoryOrder(std::ostream &out,const Phrase &phrase) const;
virtual void EvaluateInIsolation(const Phrase &source
, const TargetPhrase &targetPhrase
, ScoreComponentCollection &scoreBreakdown
, ScoreComponentCollection &estimatedScores) const;
};
}
#endif
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