moses-smt/mosesdecoder
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More reorganisation of options.

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This commit is contained in:
Ulrich Germann 2015-08-06 22:52:34 +01:00
parent 524109e2ca
commit 6c1d9e2431
16 changed files with 290 additions and 150 deletions
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14
moses-cmd/LatticeMBRGrid.cpp
View File

@ -159,13 +159,15 @@ int main(int argc, char* argv[])
}
StaticData& SD = const_cast<StaticData&>(StaticData::Instance());
SD.SetUseLatticeMBR(true);
LMBR_Options& lmbr = SD.options().lmbr;
MBR_Options& mbr = SD.options().mbr;
lmbr.enabled = true;
boost::shared_ptr<IOWrapper> ioWrapper(new IOWrapper);
if (!ioWrapper) {
throw runtime_error("Failed to initialise IOWrapper");
}
size_t nBestSize = SD.GetMBRSize();
size_t nBestSize = mbr.size;
if (nBestSize <= 0) {
throw new runtime_error("Non-positive size specified for n-best list");
@ -187,13 +189,13 @@ int main(int argc, char* argv[])
manager.CalcNBest(nBestSize, nBestList,true);
//grid search
BOOST_FOREACH(float const& p, pgrid) {
SD.SetLatticeMBRPrecision(p);
lmbr.precision = p;
BOOST_FOREACH(float const& r, rgrid) {
SD.SetLatticeMBRPRatio(r);
lmbr.ratio = r;
BOOST_FOREACH(size_t const prune_i, prune_grid) {
SD.SetLatticeMBRPruningFactor(size_t(prune_i));
lmbr.pruning_factor = prune_i;
BOOST_FOREACH(float const& scale_i, scale_grid) {
SD.SetMBRScale(scale_i);
mbr.scale = scale_i;
size_t lineCount = source->GetTranslationId();
cout << lineCount << " ||| " << p << " "
<< r << " " << size_t(prune_i) << " " << scale_i

2
moses/ChartHypothesis.cpp
View File

@ -289,7 +289,7 @@ void ChartHypothesis::CleanupArcList()
const StaticData &staticData = StaticData::Instance();
size_t nBestSize = staticData.options().nbest.nbest_size;
bool distinctNBest = (staticData.options().nbest.only_distinct
|| staticData.UseMBR()
|| staticData.options().mbr.enabled
|| staticData.GetOutputSearchGraph()
|| staticData.GetOutputSearchGraphHypergraph());

6
moses/Hypothesis.cpp
View File

@ -363,13 +363,13 @@ CleanupArcList()
*/
const StaticData &staticData = StaticData::Instance();
size_t nBestSize = staticData.options().nbest.nbest_size;
bool distinctNBest = (staticData.options().nbest.only_distinct ||
bool distinctNBest = (m_manager.options().nbest.only_distinct ||
staticData.GetLatticeSamplesSize() ||
staticData.UseMBR() ||
m_manager.options().mbr.enabled ||
staticData.GetOutputSearchGraph() ||
staticData.GetOutputSearchGraphSLF() ||
staticData.GetOutputSearchGraphHypergraph() ||
staticData.UseLatticeMBR());
m_manager.options().lmbr.enabled);
if (!distinctNBest && m_arcList->size() > nBestSize * 5) {
// prune arc list only if there too many arcs

35
moses/LatticeMBR.cpp
View File

@ -490,13 +490,18 @@ bool Edge::operator< (const Edge& compare ) const
ostream& operator<< (ostream& out, const Edge& edge)
{
out << "Head: " << edge.m_headNode->GetId() << ", Tail: " << edge.m_tailNode->GetId() << ", Score: " << edge.m_score << ", Phrase: " << edge.m_targetPhrase << endl;
out << "Head: " << edge.m_headNode->GetId()
<< ", Tail: " << edge.m_tailNode->GetId()
<< ", Score: " << edge.m_score
<< ", Phrase: " << edge.m_targetPhrase << endl;
return out;
}
bool ascendingCoverageCmp(const Hypothesis* a, const Hypothesis* b)
{
return a->GetWordsBitmap().GetNumWordsCovered() < b->GetWordsBitmap().GetNumWordsCovered();
return (a->GetWordsBitmap().GetNumWordsCovered()
<
b->GetWordsBitmap().GetNumWordsCovered());
}
void getLatticeMBRNBest(const Manager& manager, const TrellisPathList& nBestList,
@ -509,15 +514,20 @@ void getLatticeMBRNBest(const Manager& manager, const TrellisPathList& nBestList
std::map < const Hypothesis*, set <const Hypothesis*> > outgoingHyps;
map<const Hypothesis*, vector<Edge> > incomingEdges;
vector< float> estimatedScores;
manager.GetForwardBackwardSearchGraph(&connected, &connectedList, &outgoingHyps, &estimatedScores);
pruneLatticeFB(connectedList, outgoingHyps, incomingEdges, estimatedScores, manager.GetBestHypothesis(), staticData.GetLatticeMBRPruningFactor(),staticData.GetMBRScale());
manager.GetForwardBackwardSearchGraph(&connected, &connectedList,
&outgoingHyps, &estimatedScores);
LMBR_Options const& lmbr = manager.options().lmbr;
MBR_Options const& mbr = manager.options().mbr;
pruneLatticeFB(connectedList, outgoingHyps, incomingEdges, estimatedScores,
manager.GetBestHypothesis(), lmbr.pruning_factor, mbr.scale);
calcNgramExpectations(connectedList, incomingEdges, ngramPosteriors,true);
vector<float> mbrThetas = staticData.GetLatticeMBRThetas();
float p = staticData.GetLatticeMBRPrecision();
float r = staticData.GetLatticeMBRPRatio();
float mapWeight = staticData.GetLatticeMBRMapWeight();
if (mbrThetas.size() == 0) { //thetas not specified on the command line, use p and r instead
vector<float> mbrThetas = lmbr.theta;
float p = lmbr.precision;
float r = lmbr.ratio;
float mapWeight = lmbr.map_weight;
if (mbrThetas.size() == 0) {
// thetas were not specified on the command line, so use p and r instead
mbrThetas.push_back(-1); //Theta 0
mbrThetas.push_back(1/(bleu_order*p));
for (size_t i = 2; i <= bleu_order; ++i) {
@ -537,7 +547,7 @@ void getLatticeMBRNBest(const Manager& manager, const TrellisPathList& nBestList
for (iter = nBestList.begin() ; iter != nBestList.end() ; ++iter, ++ctr) {
const TrellisPath &path = **iter;
solutions.push_back(LatticeMBRSolution(path,iter==nBestList.begin()));
solutions.back().CalcScore(ngramPosteriors,mbrThetas,mapWeight);
solutions.back().CalcScore(ngramPosteriors, mbrThetas, mapWeight);
sort(solutions.begin(), solutions.end(), comparator);
while (solutions.size() > n) {
solutions.pop_back();
@ -568,7 +578,10 @@ const TrellisPath doConsensusDecoding(const Manager& manager, const TrellisPathL
map<const Hypothesis*, vector<Edge> > incomingEdges;
vector< float> estimatedScores;
manager.GetForwardBackwardSearchGraph(&connected, &connectedList, &outgoingHyps, &estimatedScores);
pruneLatticeFB(connectedList, outgoingHyps, incomingEdges, estimatedScores, manager.GetBestHypothesis(), staticData.GetLatticeMBRPruningFactor(),staticData.GetMBRScale());
LMBR_Options const& lmbr = manager.options().lmbr;
MBR_Options const& mbr = manager.options().mbr;
pruneLatticeFB(connectedList, outgoingHyps, incomingEdges, estimatedScores,
manager.GetBestHypothesis(), lmbr.pruning_factor, mbr.scale);
calcNgramExpectations(connectedList, incomingEdges, ngramExpectations,false);
//expected length is sum of expected unigram counts

16
moses/Manager.cpp
View File

@ -1492,7 +1492,7 @@ void Manager::OutputBest(OutputCollector *collector) const
// MAP decoding: best hypothesis
const Hypothesis* bestHypo = NULL;
if (!staticData.UseMBR()) {
if (!options().mbr.enabled) {
bestHypo = GetBestHypothesis();
if (bestHypo) {
if (StaticData::Instance().GetOutputHypoScore()) {
@ -1534,7 +1534,7 @@ void Manager::OutputBest(OutputCollector *collector) const
// MBR decoding (n-best MBR, lattice MBR, consensus)
else {
// we first need the n-best translations
size_t nBestSize = staticData.GetMBRSize();
size_t nBestSize = options().mbr.size;
if (nBestSize <= 0) {
cerr << "ERROR: negative size for number of MBR candidate translations not allowed (option mbr-size)" << endl;
exit(1);
@ -1547,11 +1547,11 @@ void Manager::OutputBest(OutputCollector *collector) const
}
// lattice MBR
if (staticData.UseLatticeMBR()) {
if (options().lmbr.enabled) {
if (staticData.options().nbest.enabled) {
//lattice mbr nbest
vector<LatticeMBRSolution> solutions;
size_t n = min(nBestSize, staticData.options().nbest.nbest_size);
size_t n = min(nBestSize, options().nbest.nbest_size);
getLatticeMBRNBest(*this,nBestList,solutions,n);
OutputLatticeMBRNBest(m_latticeNBestOut, solutions, translationId);
} else {
@ -1566,7 +1566,7 @@ void Manager::OutputBest(OutputCollector *collector) const
}
// consensus decoding
else if (staticData.UseConsensusDecoding()) {
else if (options().search.consensus) {
const TrellisPath &conBestHypo = doConsensusDecoding(*this,nBestList);
OutputBestHypo(conBestHypo, translationId,
staticData.GetReportSegmentation(),
@ -1608,15 +1608,15 @@ void Manager::OutputNBest(OutputCollector *collector) const
const StaticData &staticData = StaticData::Instance();
long translationId = m_source.GetTranslationId();
if (staticData.UseLatticeMBR()) {
if (options().lmbr.enabled) {
if (staticData.options().nbest.enabled) {
collector->Write(translationId, m_latticeNBestOut.str());
}
} else {
TrellisPathList nBestList;
ostringstream out;
CalcNBest(staticData.options().nbest.nbest_size, nBestList,
staticData.options().nbest.only_distinct);
CalcNBest(options().nbest.nbest_size, nBestList,
options().nbest.only_distinct);
OutputNBest(out, nBestList, staticData.GetOutputFactorOrder(),
m_source.GetTranslationId(),
staticData.GetReportSegmentation());

118
moses/StaticData.cpp
View File

@ -463,64 +463,64 @@ StaticData
}
void
StaticData
::ini_mbr_options()
{
// minimum Bayes risk decoding
m_parameter->SetParameter(m_mbr, "minimum-bayes-risk", false );
m_parameter->SetParameter<size_t>(m_mbrSize, "mbr-size", 200);
m_parameter->SetParameter(m_mbrScale, "mbr-scale", 1.0f);
}
// void
// StaticData
// ::ini_mbr_options()
// {
// // minimum Bayes risk decoding
// m_parameter->SetParameter(m_mbr, "minimum-bayes-risk", false );
// m_parameter->SetParameter<size_t>(m_mbrSize, "mbr-size", 200);
// m_parameter->SetParameter(m_mbrScale, "mbr-scale", 1.0f);
// }
void
StaticData
::ini_lmbr_options()
{
const PARAM_VEC *params;
//lattice mbr
m_parameter->SetParameter(m_useLatticeMBR, "lminimum-bayes-risk", false );
if (m_useLatticeMBR && m_mbr) {
cerr << "Error: Cannot use both n-best mbr and lattice mbr together" << endl;
exit(1);
}
// lattice MBR
if (m_useLatticeMBR) m_mbr = true;
// void
// StaticData
// ::ini_lmbr_options()
// {
// const PARAM_VEC *params;
// //lattice mbr
// // m_parameter->SetParameter(m_useLatticeMBR, "lminimum-bayes-risk", false );
// // if (m_useLatticeMBR && m_mbr) {
// // cerr << "Error: Cannot use both n-best mbr and lattice mbr together" << endl;
// // exit(1);
// // }
// // // lattice MBR
// // if (m_useLatticeMBR) m_mbr = true;
m_parameter->SetParameter<size_t>(m_lmbrPruning, "lmbr-pruning-factor", 30);
m_parameter->SetParameter(m_lmbrPrecision, "lmbr-p", 0.8f);
m_parameter->SetParameter(m_lmbrPRatio, "lmbr-r", 0.6f);
m_parameter->SetParameter(m_lmbrMapWeight, "lmbr-map-weight", 0.0f);
m_parameter->SetParameter(m_useLatticeHypSetForLatticeMBR, "lattice-hypo-set", false );
// m_parameter->SetParameter<size_t>(m_lmbrPruning, "lmbr-pruning-factor", 30);
// m_parameter->SetParameter(m_lmbrPrecision, "lmbr-p", 0.8f);
// m_parameter->SetParameter(m_lmbrPRatio, "lmbr-r", 0.6f);
// m_parameter->SetParameter(m_lmbrMapWeight, "lmbr-map-weight", 0.0f);
// m_parameter->SetParameter(m_useLatticeHypSetForLatticeMBR, "lattice-hypo-set", false );
params = m_parameter->GetParam("lmbr-thetas");
if (params) {
m_lmbrThetas = Scan<float>(*params);
}
// params = m_parameter->GetParam("lmbr-thetas");
// if (params) {
// m_lmbrThetas = Scan<float>(*params);
// }
}
// }
void
StaticData
::ini_consensus_decoding_options()
{
//consensus decoding
m_parameter->SetParameter(m_useConsensusDecoding, "consensus-decoding", false );
if (m_useConsensusDecoding && m_mbr) {
cerr<< "Error: Cannot use consensus decoding together with mbr" << endl;
exit(1);
}
if (m_useConsensusDecoding) m_mbr=true;
}
// void
// StaticData
// ::ini_consensus_decoding_options()
// {
// //consensus decoding
// m_parameter->SetParameter(m_useConsensusDecoding, "consensus-decoding", false );
// if (m_useConsensusDecoding && m_mbr) {
// cerr<< "Error: Cannot use consensus decoding together with mbr" << endl;
// exit(1);
// }
// if (m_useConsensusDecoding) m_mbr=true;
// }
void
StaticData
::ini_mira_options()
{
//mira training
m_parameter->SetParameter(m_mira, "mira", false );
}
// void
// StaticData
// ::ini_mira_options()
// {
// //mira training
// m_parameter->SetParameter(m_mira, "mira", false );
// }
bool StaticData::LoadData(Parameter *parameter)
{
@ -559,15 +559,19 @@ bool StaticData::LoadData(Parameter *parameter)
// ini_cube_pruning_options();
ini_oov_options();
ini_mbr_options();
ini_lmbr_options();
ini_consensus_decoding_options();
// ini_mbr_options();
// ini_lmbr_options();
// ini_consensus_decoding_options();
ini_mira_options();
// ini_mira_options();
// set m_nbest_options.enabled = true if necessary:
if (m_mbr || m_useLatticeMBR || m_outputSearchGraph || m_outputSearchGraphSLF
|| m_mira || m_outputSearchGraphHypergraph || m_useConsensusDecoding
if (m_options.mbr.enabled
|| m_options.mira
|| m_options.search.consensus
|| m_outputSearchGraph
|| m_outputSearchGraphSLF
|| m_outputSearchGraphHypergraph
#ifdef HAVE_PROTOBUF
|| m_outputSearchGraphPB
#endif

120
moses/StaticData.h
View File

@ -145,18 +145,18 @@ protected:
XmlInputType m_xmlInputType; //! method for handling sentence XML input
std::pair<std::string,std::string> m_xmlBrackets; //! strings to use as XML tags' opening and closing brackets. Default are "<" and ">"
bool m_mbr; //! use MBR decoder
bool m_useLatticeMBR; //! use MBR decoder
bool m_mira; // do mira training
bool m_useConsensusDecoding; //! Use Consensus decoding (DeNero et al 2009)
size_t m_mbrSize; //! number of translation candidates considered
float m_mbrScale; //! scaling factor for computing marginal probability of candidate translation
size_t m_lmbrPruning; //! average number of nodes per word wanted in pruned lattice
std::vector<float> m_lmbrThetas; //! theta(s) for lattice mbr calculation
bool m_useLatticeHypSetForLatticeMBR; //! to use nbest as hypothesis set during lattice MBR
float m_lmbrPrecision; //! unigram precision theta - see Tromble et al 08 for more details
float m_lmbrPRatio; //! decaying factor for ngram thetas - see Tromble et al 08 for more details
float m_lmbrMapWeight; //! Weight given to the map solution. See Kumar et al 09 for details
// bool m_mbr; //! use MBR decoder
// bool m_useLatticeMBR; //! use MBR decoder
// bool m_mira; // do mira training
// bool m_useConsensusDecoding; //! Use Consensus decoding (DeNero et al 2009)
// size_t m_mbrSize; //! number of translation candidates considered
// float m_mbrScale; //! scaling factor for computing marginal probability of candidate translation
// size_t m_lmbrPruning; //! average number of nodes per word wanted in pruned lattice
// std::vector<float> m_lmbrThetas; //! theta(s) for lattice mbr calculation
// bool m_useLatticeHypSetForLatticeMBR; //! to use nbest as hypothesis set during lattice MBR
// float m_lmbrPrecision; //! unigram precision theta - see Tromble et al 08 for more details
// float m_lmbrPRatio; //! decaying factor for ngram thetas - see Tromble et al 08 for more details
// float m_lmbrMapWeight; //! Weight given to the map solution. See Kumar et al 09 for details
size_t m_lmcache_cleanup_threshold; //! number of translations after which LM claenup is performed (0=never, N=after N translations; default is 1)
bool m_lmEnableOOVFeature;
@ -512,55 +512,55 @@ public:
const std::string& GetFactorDelimiter() const {
return m_factorDelimiter;
}
bool UseMBR() const {
return m_mbr;
}
bool UseLatticeMBR() const {
return m_useLatticeMBR ;
}
bool UseConsensusDecoding() const {
return m_useConsensusDecoding;
}
void SetUseLatticeMBR(bool flag) {
m_useLatticeMBR = flag;
}
size_t GetMBRSize() const {
return m_mbrSize;
}
float GetMBRScale() const {
return m_mbrScale;
}
void SetMBRScale(float scale) {
m_mbrScale = scale;
}
size_t GetLatticeMBRPruningFactor() const {
return m_lmbrPruning;
}
void SetLatticeMBRPruningFactor(size_t prune) {
m_lmbrPruning = prune;
}
const std::vector<float>& GetLatticeMBRThetas() const {
return m_lmbrThetas;
}
bool UseLatticeHypSetForLatticeMBR() const {
return m_useLatticeHypSetForLatticeMBR;
}
float GetLatticeMBRPrecision() const {
return m_lmbrPrecision;
}
void SetLatticeMBRPrecision(float p) {
m_lmbrPrecision = p;
}
float GetLatticeMBRPRatio() const {
return m_lmbrPRatio;
}
void SetLatticeMBRPRatio(float r) {
m_lmbrPRatio = r;
}
// bool UseMBR() const {
// return m_mbr;
// }
// bool UseLatticeMBR() const {
// return m_useLatticeMBR ;
// }
// bool UseConsensusDecoding() const {
// return m_useConsensusDecoding;
// }
// void SetUseLatticeMBR(bool flag) {
// m_useLatticeMBR = flag;
// }
// size_t GetMBRSize() const {
// return m_mbrSize;
// }
// float GetMBRScale() const {
// return m_mbrScale;
// }
// void SetMBRScale(float scale) {
// m_mbrScale = scale;
// }
// size_t GetLatticeMBRPruningFactor() const {
// return m_lmbrPruning;
// }
// void SetLatticeMBRPruningFactor(size_t prune) {
// m_lmbrPruning = prune;
// }
// const std::vector<float>& GetLatticeMBRThetas() const {
// return m_lmbrThetas;
// }
// bool UseLatticeHypSetForLatticeMBR() const {
// return m_useLatticeHypSetForLatticeMBR;
// }
// float GetLatticeMBRPrecision() const {
// return m_lmbrPrecision;
// }
// void SetLatticeMBRPrecision(float p) {
// m_lmbrPrecision = p;
// }
// float GetLatticeMBRPRatio() const {
// return m_lmbrPRatio;
// }
// void SetLatticeMBRPRatio(float r) {
// m_lmbrPRatio = r;
// }
float GetLatticeMBRMapWeight() const {
return m_lmbrMapWeight;
}
// float GetLatticeMBRMapWeight() const {
// return m_lmbrMapWeight;
// }
// bool UseTimeout() const {
// return m_timeout;

7
moses/mbr.cpp
View File

@ -92,7 +92,7 @@ float calculate_score(const vector< vector<const Factor*> > & sents, int ref, in
const TrellisPath doMBR(const TrellisPathList& nBestList)
{
float marginal = 0;
float mbr_scale = StaticData::Instance().options().mbr.scale;
vector<float> joint_prob_vec;
vector< vector<const Factor*> > translations;
float joint_prob;
@ -104,14 +104,13 @@ const TrellisPath doMBR(const TrellisPathList& nBestList)
float maxScore = -1e20;
for (iter = nBestList.begin() ; iter != nBestList.end() ; ++iter) {
const TrellisPath &path = **iter;
float score = StaticData::Instance().GetMBRScale()
* path.GetScoreBreakdown()->GetWeightedScore();
float score = mbr_scale * path.GetScoreBreakdown()->GetWeightedScore();
if (maxScore < score) maxScore = score;
}
for (iter = nBestList.begin() ; iter != nBestList.end() ; ++iter) {
const TrellisPath &path = **iter;
joint_prob = UntransformScore(StaticData::Instance().GetMBRScale() * path.GetScoreBreakdown()->GetWeightedScore() - maxScore);
joint_prob = UntransformScore(mbr_scale * path.GetScoreBreakdown()->GetWeightedScore() - maxScore);
marginal += joint_prob;
joint_prob_vec.push_back(joint_prob);

25
moses/parameters/AllOptions.cpp
View File

@ -19,6 +19,11 @@ namespace Moses
if (!reordering.init(param)) return false;
if (!context.init(param)) return false;
if (!input.init(param)) return false;
if (!mbr.init(param)) return false;
if (!lmbr.init(param)) return false;
param.SetParameter(mira, "mira", false);
return sanity_check();
}
@ -26,6 +31,26 @@ namespace Moses
AllOptions::
sanity_check()
{
using namespace std;
if (lmbr.enabled)
{
if (mbr.enabled)
{
cerr << "Error: Cannot use both n-best mbr and lattice mbr together" << endl;
return false;
}
mbr.enabled = true;
}
if (search.consensus)
{
if (mbr.enabled)
{
cerr << "Error: Cannot use consensus decoding together with mbr" << endl;
return false;
}
mbr.enabled = true;
}
return true;
}
}

7
moses/parameters/AllOptions.h
View File

@ -8,6 +8,8 @@
#include "ReorderingOptions.h"
#include "ContextParameters.h"
#include "InputOptions.h"
#include "MBR_Options.h"
#include "LMBR_Options.h"
namespace Moses
{
@ -20,6 +22,11 @@ namespace Moses
ReorderingOptions reordering;
ContextParameters context;
InputOptions input;
MBR_Options mbr;
LMBR_Options lmbr;
bool mira;
// StackOptions stack;
// BeamSearchOptions beam;
bool init(Parameter const& param);

24
moses/parameters/LMBR_Options.cpp Normal file
View File

@ -0,0 +1,24 @@
// -*- mode: c++; indent-tabs-mode: nil; tab-width: 2 -*-
#include "LMBR_Options.h"
namespace Moses {
bool
LMBR_Options::
init(Parameter const& param)
{
param.SetParameter(enabled, "lminimum-bayes-risk", false);
param.SetParameter(ratio, "lmbr-r", 0.6f);
param.SetParameter(precision, "lmbr-p", 0.8f);
param.SetParameter(map_weight, "lmbr-map-weight", 0.0f);
param.SetParameter(pruning_factor, "lmbr-pruning-factor", size_t(30));
param.SetParameter(use_lattice_hyp_set, "lattice-hypo-set", false);
PARAM_VEC const* params = param.GetParam("lmbr-thetas");
if (params) theta = Scan<float>(*params);
return true;
}
}

25
moses/parameters/LMBR_Options.h Normal file
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@ -0,0 +1,25 @@
// -*- mode: c++; indent-tabs-mode: nil; tab-width: 2 -*-
#pragma once
#include <string>
#include <vector>
#include "moses/Parameter.h"
namespace Moses
{
// Options for mimum bayes risk decoding
struct
LMBR_Options
{
bool enabled;
bool use_lattice_hyp_set; //! to use nbest as hypothesis set during lattice MBR
float precision; //! unigram precision theta - see Tromble et al 08 for more details
float ratio; //! decaying factor for ngram thetas - see Tromble et al 08
float map_weight; //! Weight given to the map solution. See Kumar et al 09
size_t pruning_factor; //! average number of nodes per word wanted in pruned lattice
std::vector<float> theta; //! theta(s) for lattice mbr calculation
bool init(Parameter const& param);
LMBR_Options() {}
};
}

16
moses/parameters/MBR_Options.cpp Normal file
View File

@ -0,0 +1,16 @@
// -*- mode: c++; indent-tabs-mode: nil; tab-width: 2 -*-
#include "MBR_Options.h"
namespace Moses {
bool
MBR_Options::
init(Parameter const& param)
{
param.SetParameter(enabled, "minimum-bayes-risk", false);
param.SetParameter<size_t>(size, "mbr-size", 200);
param.SetParameter(scale, "mbr-scale", 1.0f);
return true;
}
}

21
moses/parameters/MBR_Options.h Normal file
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@ -0,0 +1,21 @@
// -*- mode: c++; indent-tabs-mode: nil; tab-width: 2 -*-
#pragma once
#include <string>
#include "moses/Parameter.h"
namespace Moses
{
// Options for mimum bayes risk decoding
struct
MBR_Options
{
bool enabled;
size_t size; //! number of translation candidates considered
float scale; /*! scaling factor for computing marginal probability
* of candidate translation */
bool init(Parameter const& param);
MBR_Options() {}
};
}

2
moses/parameters/SearchOptions.cpp
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@ -31,6 +31,8 @@ namespace Moses
DEFAULT_MAX_PART_TRANS_OPT_SIZE);
param.SetParameter(consensus, "consensus-decoding", false);
// transformation to log of a few scores
beam_width = TransformScore(beam_width);
trans_opt_threshold = TransformScore(trans_opt_threshold);

2
moses/parameters/SearchOptions.h
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@ -24,6 +24,8 @@ namespace Moses
int timeout;
bool consensus; //! Use Consensus decoding (DeNero et al 2009)
// reordering options
// bool reorderingConstraint; //! use additional reordering constraints
// bool useEarlyDistortionCost;