mosesdecoder/moses/Hypothesis.cpp

624 lines
19 KiB
C++

// -*- mode: c++; indent-tabs-mode: nil; tab-width:2 -*-
// 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 <iostream>
#include <limits>
#include <vector>
#include <algorithm>
#include "TranslationOption.h"
#include "TranslationOptionCollection.h"
#include "Hypothesis.h"
#include "Util.h"
#include "SquareMatrix.h"
#include "StaticData.h"
#include "InputType.h"
#include "Manager.h"
#include "IOWrapper.h"
#include "moses/FF/FFState.h"
#include "moses/FF/StatefulFeatureFunction.h"
#include "moses/FF/StatelessFeatureFunction.h"
#include <boost/foreach.hpp>
using namespace std;
namespace Moses
{
Hypothesis::
Hypothesis(Manager& manager, InputType const& source, const TranslationOption &initialTransOpt, const Bitmap &bitmap)
: m_prevHypo(NULL)
, m_sourceCompleted(bitmap)
, m_sourceInput(source)
, m_currSourceWordsRange(
m_sourceCompleted.GetFirstGapPos()>0 ? 0 : NOT_FOUND,
m_sourceCompleted.GetFirstGapPos()>0 ? m_sourceCompleted.GetFirstGapPos()-1 : NOT_FOUND)
, m_currTargetWordsRange(NOT_FOUND, NOT_FOUND)
, m_wordDeleted(false)
, m_totalScore(0.0f)
, m_futureScore(0.0f)
, m_ffStates(StatefulFeatureFunction::GetStatefulFeatureFunctions().size())
, m_arcList(NULL)
, m_transOpt(initialTransOpt)
, m_manager(manager)
, m_id(m_manager.GetNextHypoId())
{
// used for initial seeding of trans process
// initialize scores
//_hash_computed = false;
//s_HypothesesCreated = 1;
const vector<const StatefulFeatureFunction*>& ffs = StatefulFeatureFunction::GetStatefulFeatureFunctions();
for (unsigned i = 0; i < ffs.size(); ++i)
m_ffStates[i] = ffs[i]->EmptyHypothesisState(source);
m_manager.GetSentenceStats().AddCreated();
}
/***
* continue prevHypo by appending the phrases in transOpt
*/
Hypothesis::
Hypothesis(const Hypothesis &prevHypo, const TranslationOption &transOpt, const Bitmap &bitmap)
: m_prevHypo(&prevHypo)
, m_sourceCompleted(bitmap)
, m_sourceInput(prevHypo.m_sourceInput)
, m_currSourceWordsRange(transOpt.GetSourceWordsRange())
, m_currTargetWordsRange(prevHypo.m_currTargetWordsRange.GetEndPos() + 1,
prevHypo.m_currTargetWordsRange.GetEndPos()
+ transOpt.GetTargetPhrase().GetSize())
, m_wordDeleted(false)
, m_totalScore(0.0f)
, m_futureScore(0.0f)
, m_ffStates(prevHypo.m_ffStates.size())
, m_arcList(NULL)
, m_transOpt(transOpt)
, m_manager(prevHypo.GetManager())
, m_id(m_manager.GetNextHypoId())
{
m_currScoreBreakdown.PlusEquals(transOpt.GetScoreBreakdown());
m_wordDeleted = transOpt.IsDeletionOption();
m_manager.GetSentenceStats().AddCreated();
}
Hypothesis::
~Hypothesis()
{
for (unsigned i = 0; i < m_ffStates.size(); ++i)
delete m_ffStates[i];
if (m_arcList) {
ArcList::iterator iter;
for (iter = m_arcList->begin() ; iter != m_arcList->end() ; ++iter) {
delete *iter;
}
m_arcList->clear();
delete m_arcList;
m_arcList = NULL;
}
}
void
Hypothesis::
AddArc(Hypothesis *loserHypo)
{
if (!m_arcList) {
if (loserHypo->m_arcList) { // we don't have an arcList, but loser does
this->m_arcList = loserHypo->m_arcList; // take ownership, we'll delete
loserHypo->m_arcList = 0; // prevent a double deletion
} else {
this->m_arcList = new ArcList();
}
} else {
if (loserHypo->m_arcList) { // both have an arc list: merge. delete loser
size_t my_size = m_arcList->size();
size_t add_size = loserHypo->m_arcList->size();
this->m_arcList->resize(my_size + add_size, 0);
std::memcpy(&(*m_arcList)[0] + my_size, &(*loserHypo->m_arcList)[0], add_size * sizeof(Hypothesis *));
delete loserHypo->m_arcList;
loserHypo->m_arcList = 0;
} else { // loserHypo doesn't have any arcs
// DO NOTHING
}
}
m_arcList->push_back(loserHypo);
}
void
Hypothesis::
EvaluateWhenApplied(StatefulFeatureFunction const& sfff, int state_idx)
{
const StaticData &staticData = StaticData::Instance();
if (! staticData.IsFeatureFunctionIgnored( sfff )) {
// Manager& manager = this->GetManager(); //Get the manager and the ttask
// ttasksptr const& ttask = manager.GetTtask();
FFState const* prev = m_prevHypo ? m_prevHypo->m_ffStates[state_idx] : NULL;
m_ffStates[state_idx]
= sfff.EvaluateWhenApplied(*this, prev, &m_currScoreBreakdown);
}
}
void
Hypothesis::
EvaluateWhenApplied(const StatelessFeatureFunction& slff)
{
const StaticData &staticData = StaticData::Instance();
if (! staticData.IsFeatureFunctionIgnored( slff )) {
slff.EvaluateWhenApplied(*this, &m_currScoreBreakdown);
}
}
/***
* calculate the logarithm of our total translation score (sum up components)
*/
void
Hypothesis::
EvaluateWhenApplied(float futureScore)
{
IFVERBOSE(2) {
m_manager.GetSentenceStats().StartTimeOtherScore();
}
// some stateless score producers cache their values in the translation
// option: add these here
// language model scores for n-grams completely contained within a target
// phrase are also included here
// compute values of stateless feature functions that were not
// cached in the translation option
const vector<const StatelessFeatureFunction*>& sfs =
StatelessFeatureFunction::GetStatelessFeatureFunctions();
for (unsigned i = 0; i < sfs.size(); ++i) {
const StatelessFeatureFunction &ff = *sfs[i];
EvaluateWhenApplied(ff);
}
const vector<const StatefulFeatureFunction*>& ffs =
StatefulFeatureFunction::GetStatefulFeatureFunctions();
for (unsigned i = 0; i < ffs.size(); ++i) {
const StatefulFeatureFunction &ff = *ffs[i];
const StaticData &staticData = StaticData::Instance();
if (! staticData.IsFeatureFunctionIgnored(ff)) {
FFState const* s = m_prevHypo ? m_prevHypo->m_ffStates[i] : NULL;
m_ffStates[i] = ff.EvaluateWhenApplied(*this, s, &m_currScoreBreakdown);
}
}
IFVERBOSE(2) {
m_manager.GetSentenceStats().StopTimeOtherScore();
m_manager.GetSentenceStats().StartTimeEstimateScore();
}
// FUTURE COST
m_futureScore = futureScore;
// TOTAL
m_totalScore = m_currScoreBreakdown.GetWeightedScore() + m_futureScore;
if (m_prevHypo) m_totalScore += m_prevHypo->GetScore();
IFVERBOSE(2) {
m_manager.GetSentenceStats().StopTimeEstimateScore();
}
}
const Hypothesis* Hypothesis::GetPrevHypo()const
{
return m_prevHypo;
}
/**
* print hypothesis information for pharaoh-style logging
*/
void
Hypothesis::
PrintHypothesis() const
{
if (!m_prevHypo) {
TRACE_ERR(endl << "NULL hypo" << endl);
return;
}
TRACE_ERR(endl << "creating hypothesis "<< m_id <<" from "<< m_prevHypo->m_id<<" ( ");
int end = (int)(m_prevHypo->GetCurrTargetPhrase().GetSize()-1);
int start = end-1;
if ( start < 0 ) start = 0;
if ( m_prevHypo->m_currTargetWordsRange.GetStartPos() == NOT_FOUND ) {
TRACE_ERR( "<s> ");
} else {
TRACE_ERR( "... ");
}
if (end>=0) {
Range range(start, end);
TRACE_ERR( m_prevHypo->GetCurrTargetPhrase().GetSubString(range) << " ");
}
TRACE_ERR( ")"<<endl);
TRACE_ERR( "\tbase score "<< (m_prevHypo->m_totalScore - m_prevHypo->m_futureScore) <<endl);
TRACE_ERR( "\tcovering "<<m_currSourceWordsRange.GetStartPos()<<"-"<<m_currSourceWordsRange.GetEndPos()
<<": " << m_transOpt.GetInputPath().GetPhrase() << endl);
TRACE_ERR( "\ttranslated as: "<<(Phrase&) GetCurrTargetPhrase()<<endl); // <<" => translation cost "<<m_score[ScoreType::PhraseTrans];
if (m_wordDeleted) TRACE_ERR( "\tword deleted"<<endl);
// TRACE_ERR( "\tdistance: "<<GetCurrSourceWordsRange().CalcDistortion(m_prevHypo->GetCurrSourceWordsRange())); // << " => distortion cost "<<(m_score[ScoreType::Distortion]*weightDistortion)<<endl;
// TRACE_ERR( "\tlanguage model cost "); // <<m_score[ScoreType::LanguageModelScore]<<endl;
// TRACE_ERR( "\tword penalty "); // <<(m_score[ScoreType::WordPenalty]*weightWordPenalty)<<endl;
TRACE_ERR( "\tscore "<<m_totalScore - m_futureScore<<" + future cost "<<m_futureScore<<" = "<<m_totalScore<<endl);
TRACE_ERR( "\tunweighted feature scores: " << m_currScoreBreakdown << endl);
//PrintLMScores();
}
void
Hypothesis::
CleanupArcList()
{
// point this hypo's main hypo to itself
SetWinningHypo(this);
if (!m_arcList) return;
/* keep only number of arcs we need to create all n-best paths.
* However, may not be enough if only unique candidates are needed,
* so we'll keep all of arc list if nedd distinct n-best list
*/
const StaticData &staticData = StaticData::Instance();
AllOptions const& opts = m_manager.options();
size_t nBestSize = opts.nbest.nbest_size;
bool distinctNBest = opts.NBestDistinct();
if (!distinctNBest && m_arcList->size() > nBestSize * 5) {
// prune arc list only if there too many arcs
NTH_ELEMENT4(m_arcList->begin(), m_arcList->begin() + nBestSize - 1,
m_arcList->end(), CompareHypothesisTotalScore());
// delete bad ones
ArcList::iterator i = m_arcList->begin() + nBestSize;
while (i != m_arcList->end()) delete *i++;
m_arcList->erase(m_arcList->begin() + nBestSize, m_arcList->end());
}
// set all arc's main hypo variable to this hypo
ArcList::iterator iter = m_arcList->begin();
for (; iter != m_arcList->end() ; ++iter) {
Hypothesis *arc = *iter;
arc->SetWinningHypo(this);
}
}
TargetPhrase const&
Hypothesis::
GetCurrTargetPhrase() const
{
return m_transOpt.GetTargetPhrase();
}
void
Hypothesis::
GetOutputPhrase(Phrase &out) const
{
if (m_prevHypo != NULL)
m_prevHypo->GetOutputPhrase(out);
out.Append(GetCurrTargetPhrase());
}
TO_STRING_BODY(Hypothesis)
// friend
ostream& operator<<(ostream& out, const Hypothesis& hypo)
{
hypo.ToStream(out);
// words bitmap
out << "[" << hypo.m_sourceCompleted << "] ";
// scores
out << " [total=" << hypo.GetTotalScore() << "]";
out << " " << hypo.GetScoreBreakdown();
// alignment
out << " " << hypo.GetCurrTargetPhrase().GetAlignNonTerm();
return out;
}
std::string
Hypothesis::
GetSourcePhraseStringRep(const vector<FactorType> factorsToPrint) const
{
return m_transOpt.GetInputPath().GetPhrase().GetStringRep(factorsToPrint);
}
std::string
Hypothesis::
GetTargetPhraseStringRep(const vector<FactorType> factorsToPrint) const
{
return (m_prevHypo ? GetCurrTargetPhrase().GetStringRep(factorsToPrint) : "");
}
std::string
Hypothesis::
GetSourcePhraseStringRep() const
{
vector<FactorType> allFactors(MAX_NUM_FACTORS);
for(size_t i=0; i < MAX_NUM_FACTORS; i++)
allFactors[i] = i;
return GetSourcePhraseStringRep(allFactors);
}
std::string
Hypothesis::
GetTargetPhraseStringRep() const
{
vector<FactorType> allFactors(MAX_NUM_FACTORS);
for(size_t i=0; i < MAX_NUM_FACTORS; i++)
allFactors[i] = i;
return GetTargetPhraseStringRep(allFactors);
}
void
Hypothesis::
OutputAlignment(std::ostream &out) const
{
std::vector<const Hypothesis *> edges;
const Hypothesis *currentHypo = this;
while (currentHypo) {
edges.push_back(currentHypo);
currentHypo = currentHypo->GetPrevHypo();
}
OutputAlignment(out, edges, m_manager.options().output.WA_SortOrder);
}
void
Hypothesis::
OutputAlignment(ostream &out,
vector<const Hypothesis *> const& edges,
WordAlignmentSort waso)
{
size_t targetOffset = 0;
for (int currEdge = (int)edges.size() - 1 ; currEdge >= 0 ; currEdge--) {
const Hypothesis &edge = *edges[currEdge];
const TargetPhrase &tp = edge.GetCurrTargetPhrase();
size_t sourceOffset = edge.GetCurrSourceWordsRange().GetStartPos();
OutputAlignment(out, tp.GetAlignTerm(), sourceOffset, targetOffset, waso);
targetOffset += tp.GetSize();
}
// Used by --print-alignment-info, so no endl
}
void
Hypothesis::
OutputAlignment(ostream &out, const AlignmentInfo &ai,
size_t sourceOffset, size_t targetOffset,
WordAlignmentSort waso)
{
typedef std::vector< const std::pair<size_t,size_t>* > AlignVec;
AlignVec alignments = ai.GetSortedAlignments(waso);
AlignVec::const_iterator it;
for (it = alignments.begin(); it != alignments.end(); ++it) {
const std::pair<size_t,size_t> &alignment = **it;
out << alignment.first + sourceOffset << "-"
<< alignment.second + targetOffset << " ";
}
}
void
Hypothesis::
OutputInput(std::vector<const Phrase*>& map, const Hypothesis* hypo)
{
if (!hypo->GetPrevHypo()) return;
OutputInput(map, hypo->GetPrevHypo());
map[hypo->GetCurrSourceWordsRange().GetStartPos()]
= &hypo->GetTranslationOption().GetInputPath().GetPhrase();
}
void
Hypothesis::
OutputInput(std::ostream& os) const
{
size_t len = this->GetInput().GetSize();
std::vector<const Phrase*> inp_phrases(len, 0);
OutputInput(inp_phrases, this);
for (size_t i=0; i<len; ++i)
if (inp_phrases[i]) os << *inp_phrases[i];
}
void
Hypothesis::
OutputBestSurface(std::ostream &out, const std::vector<FactorType> &outputFactorOrder,
char reportSegmentation, bool reportAllFactors) const
{
if (m_prevHypo) {
// recursively retrace this best path through the lattice, starting from the end of the hypothesis sentence
m_prevHypo->OutputBestSurface(out, outputFactorOrder, reportSegmentation, reportAllFactors);
}
OutputSurface(out, *this, outputFactorOrder, reportSegmentation, reportAllFactors);
}
//////////////////////////////////////////////////////////////////////////
/***
* print surface factor only for the given phrase
*/
void
Hypothesis::
OutputSurface(std::ostream &out, const Hypothesis &edge,
const std::vector<FactorType> &outputFactorOrder,
char reportSegmentation, bool reportAllFactors) const
{
UTIL_THROW_IF2(outputFactorOrder.size() == 0,
"Must specific at least 1 output factor");
const TargetPhrase& phrase = edge.GetCurrTargetPhrase();
bool markUnknown = StaticData::Instance().GetMarkUnknown();
if (reportAllFactors == true) {
out << phrase;
} else {
FactorType placeholderFactor = StaticData::Instance().GetPlaceholderFactor();
std::map<size_t, const Factor*> placeholders;
if (placeholderFactor != NOT_FOUND) {
// creates map of target position -> factor for placeholders
placeholders = GetPlaceholders(edge, placeholderFactor);
}
size_t size = phrase.GetSize();
for (size_t pos = 0 ; pos < size ; pos++) {
const Factor *factor = phrase.GetFactor(pos, outputFactorOrder[0]);
if (placeholders.size()) {
// do placeholders
std::map<size_t, const Factor*>::const_iterator iter = placeholders.find(pos);
if (iter != placeholders.end()) {
factor = iter->second;
}
}
UTIL_THROW_IF2(factor == NULL,
"No factor 0 at position " << pos);
//preface surface form with UNK if marking unknowns
const Word &word = phrase.GetWord(pos);
if(markUnknown && word.IsOOV()) {
out << StaticData::Instance().GetUnknownWordPrefix()
<< *factor
<< StaticData::Instance().GetUnknownWordSuffix();
} else {
out << *factor;
}
for (size_t i = 1 ; i < outputFactorOrder.size() ; i++) {
const Factor *factor = phrase.GetFactor(pos, outputFactorOrder[i]);
UTIL_THROW_IF2(factor == NULL,
"No factor " << i << " at position " << pos);
out << "|" << *factor;
}
out << " ";
}
}
// trace ("report segmentation") option "-t" / "-tt"
if (reportSegmentation > 0 && phrase.GetSize() > 0) {
const Range &sourceRange = edge.GetCurrSourceWordsRange();
const int sourceStart = sourceRange.GetStartPos();
const int sourceEnd = sourceRange.GetEndPos();
out << "|" << sourceStart << "-" << sourceEnd; // enriched "-tt"
if (reportSegmentation == 2) {
WordAlignmentSort waso = m_manager.options().output.WA_SortOrder;
out << ",wa=";
const AlignmentInfo &ai = edge.GetCurrTargetPhrase().GetAlignTerm();
Hypothesis::OutputAlignment(out, ai, 0, 0, waso);
out << ",total=";
out << edge.GetScore() - edge.GetPrevHypo()->GetScore();
out << ",";
ScoreComponentCollection scoreBreakdown(edge.GetScoreBreakdown());
scoreBreakdown.MinusEquals(edge.GetPrevHypo()->GetScoreBreakdown());
bool with_labels = m_manager.options().nbest.include_feature_labels;
scoreBreakdown.OutputAllFeatureScores(out, with_labels);
}
out << "| ";
}
}
std::map<size_t, const Factor*>
Hypothesis::
GetPlaceholders(const Hypothesis &hypo, FactorType placeholderFactor) const
{
const InputPath &inputPath = hypo.GetTranslationOption().GetInputPath();
const Phrase &inputPhrase = inputPath.GetPhrase();
std::map<size_t, const Factor*> ret;
for (size_t sourcePos = 0; sourcePos < inputPhrase.GetSize(); ++sourcePos) {
const Factor *factor = inputPhrase.GetFactor(sourcePos, placeholderFactor);
if (factor) {
std::set<size_t> targetPos = hypo.GetTranslationOption().GetTargetPhrase().GetAlignTerm().GetAlignmentsForSource(sourcePos);
UTIL_THROW_IF2(targetPos.size() != 1,
"Placeholder should be aligned to 1, and only 1, word");
ret[*targetPos.begin()] = factor;
}
}
return ret;
}
size_t Hypothesis::hash() const
{
size_t seed;
// coverage NOTE from Hieu - we could make bitmap comparison here
// and in operator== compare the pointers since the bitmaps come
// from a factory. Same coverage is guaranteed to have the same
// bitmap. However, this make the decoding algorithm
// non-deterministic as the order of hypo extension can be
// different. This causes several regression tests to break. Since
// the speedup is minimal, I'm gonna leave it comparing the actual
// bitmaps
seed = m_sourceCompleted.hash();
// states
for (size_t i = 0; i < m_ffStates.size(); ++i) {
const FFState *state = m_ffStates[i];
size_t hash = state->hash();
boost::hash_combine(seed, hash);
}
return seed;
}
bool Hypothesis::operator==(const Hypothesis& other) const
{
// coverage
if (m_sourceCompleted != other.m_sourceCompleted) {
return false;
}
// states
for (size_t i = 0; i < m_ffStates.size(); ++i) {
const FFState &thisState = *m_ffStates[i];
const FFState &otherState = *other.m_ffStates[i];
if (thisState != otherState) {
return false;
}
}
return true;
}
bool
Hypothesis::
beats(Hypothesis const& b) const
{
if (m_totalScore != b.m_totalScore)
return m_totalScore > b.m_totalScore;
else if (m_futureScore != b.m_futureScore)
return m_futureScore > b.m_futureScore;
else if (m_prevHypo)
return b.m_prevHypo ? m_prevHypo->beats(*b.m_prevHypo) : true;
else return false;
// TO DO: add more tie breaking here
// results. We should compare other property of the hypos here.
// On the other hand, how likely is this going to happen?
}
}