mirror of
https://github.com/moses-smt/mosesdecoder.git
synced 2024-09-11 11:25:40 +03:00
681 lines
25 KiB
C++
681 lines
25 KiB
C++
/*
|
|
* LatticeMBR.cpp
|
|
* moses-cmd
|
|
*
|
|
* Created by Abhishek Arun on 26/01/2010.
|
|
* Copyright 2010 __MyCompanyName__. All rights reserved.
|
|
*
|
|
*/
|
|
|
|
#include "LatticeMBR.h"
|
|
#include "moses/StaticData.h"
|
|
#include <algorithm>
|
|
#include <set>
|
|
|
|
using namespace std;
|
|
|
|
namespace Moses
|
|
{
|
|
|
|
size_t bleu_order = 4;
|
|
float UNKNGRAMLOGPROB = -20;
|
|
void GetOutputWords(const TrellisPath &path, vector <Word> &translation)
|
|
{
|
|
const std::vector<const Hypothesis *> &edges = path.GetEdges();
|
|
|
|
// print the surface factor of the translation
|
|
for (int currEdge = (int)edges.size() - 1 ; currEdge >= 0 ; currEdge--) {
|
|
const Hypothesis &edge = *edges[currEdge];
|
|
const Phrase &phrase = edge.GetCurrTargetPhrase();
|
|
size_t size = phrase.GetSize();
|
|
for (size_t pos = 0 ; pos < size ; pos++) {
|
|
translation.push_back(phrase.GetWord(pos));
|
|
}
|
|
}
|
|
}
|
|
|
|
|
|
void extract_ngrams(const vector<Word >& sentence, map < Phrase, int > & allngrams)
|
|
{
|
|
for (int k = 0; k < (int)bleu_order; k++) {
|
|
for(int i =0; i < max((int)sentence.size()-k,0); i++) {
|
|
Phrase ngram( k+1);
|
|
for ( int j = i; j<= i+k; j++) {
|
|
ngram.AddWord(sentence[j]);
|
|
}
|
|
++allngrams[ngram];
|
|
}
|
|
}
|
|
}
|
|
|
|
|
|
|
|
void NgramScores::addScore(const Hypothesis* node, const Phrase& ngram, float score)
|
|
{
|
|
set<Phrase>::const_iterator ngramIter = m_ngrams.find(ngram);
|
|
if (ngramIter == m_ngrams.end()) {
|
|
ngramIter = m_ngrams.insert(ngram).first;
|
|
}
|
|
map<const Phrase*,float>& ngramScores = m_scores[node];
|
|
map<const Phrase*,float>::iterator scoreIter = ngramScores.find(&(*ngramIter));
|
|
if (scoreIter == ngramScores.end()) {
|
|
ngramScores[&(*ngramIter)] = score;
|
|
} else {
|
|
ngramScores[&(*ngramIter)] = log_sum(score,scoreIter->second);
|
|
}
|
|
}
|
|
|
|
NgramScores::NodeScoreIterator NgramScores::nodeBegin(const Hypothesis* node)
|
|
{
|
|
return m_scores[node].begin();
|
|
}
|
|
|
|
|
|
NgramScores::NodeScoreIterator NgramScores::nodeEnd(const Hypothesis* node)
|
|
{
|
|
return m_scores[node].end();
|
|
}
|
|
|
|
LatticeMBRSolution::LatticeMBRSolution(const TrellisPath& path, bool isMap) :
|
|
m_score(0.0f)
|
|
{
|
|
const std::vector<const Hypothesis *> &edges = path.GetEdges();
|
|
|
|
for (int currEdge = (int)edges.size() - 1 ; currEdge >= 0 ; currEdge--) {
|
|
const Hypothesis &edge = *edges[currEdge];
|
|
const Phrase &phrase = edge.GetCurrTargetPhrase();
|
|
size_t size = phrase.GetSize();
|
|
for (size_t pos = 0 ; pos < size ; pos++) {
|
|
m_words.push_back(phrase.GetWord(pos));
|
|
}
|
|
}
|
|
if (isMap) {
|
|
m_mapScore = path.GetTotalScore();
|
|
} else {
|
|
m_mapScore = 0;
|
|
}
|
|
}
|
|
|
|
|
|
void LatticeMBRSolution::CalcScore(map<Phrase, float>& finalNgramScores, const vector<float>& thetas, float mapWeight)
|
|
{
|
|
m_ngramScores.assign(thetas.size()-1, -10000);
|
|
|
|
map < Phrase, int > counts;
|
|
extract_ngrams(m_words,counts);
|
|
|
|
//Now score this translation
|
|
m_score = thetas[0] * m_words.size();
|
|
|
|
//Calculate the ngramScores, working in log space at first
|
|
for (map < Phrase, int >::iterator ngrams = counts.begin(); ngrams != counts.end(); ++ngrams) {
|
|
float ngramPosterior = UNKNGRAMLOGPROB;
|
|
map<Phrase,float>::const_iterator ngramPosteriorIt = finalNgramScores.find(ngrams->first);
|
|
if (ngramPosteriorIt != finalNgramScores.end()) {
|
|
ngramPosterior = ngramPosteriorIt->second;
|
|
}
|
|
size_t ngramSize = ngrams->first.GetSize();
|
|
m_ngramScores[ngramSize-1] = log_sum(log((float)ngrams->second) + ngramPosterior,m_ngramScores[ngramSize-1]);
|
|
}
|
|
|
|
//convert from log to probability and create weighted sum
|
|
for (size_t i = 0; i < m_ngramScores.size(); ++i) {
|
|
m_ngramScores[i] = exp(m_ngramScores[i]);
|
|
m_score += thetas[i+1] * m_ngramScores[i];
|
|
}
|
|
|
|
|
|
//The map score
|
|
m_score += m_mapScore*mapWeight;
|
|
}
|
|
|
|
|
|
void pruneLatticeFB(Lattice & connectedHyp, map < const Hypothesis*, set <const Hypothesis* > > & outgoingHyps, map<const Hypothesis*, vector<Edge> >& incomingEdges,
|
|
const vector< float> & estimatedScores, const Hypothesis* bestHypo, size_t edgeDensity, float scale)
|
|
{
|
|
|
|
//Need hyp 0 in connectedHyp - Find empty hypothesis
|
|
VERBOSE(2,"Pruning lattice to edge density " << edgeDensity << endl);
|
|
const Hypothesis* emptyHyp = connectedHyp.at(0);
|
|
while (emptyHyp->GetId() != 0) {
|
|
emptyHyp = emptyHyp->GetPrevHypo();
|
|
}
|
|
connectedHyp.push_back(emptyHyp); //Add it to list of hyps
|
|
|
|
//Need hyp 0's outgoing Hyps
|
|
for (size_t i = 0; i < connectedHyp.size(); ++i) {
|
|
if (connectedHyp[i]->GetId() > 0 && connectedHyp[i]->GetPrevHypo()->GetId() == 0)
|
|
outgoingHyps[emptyHyp].insert(connectedHyp[i]);
|
|
}
|
|
|
|
//sort hyps based on estimated scores - do so by copying to multimap
|
|
multimap<float, const Hypothesis*> sortHypsByVal;
|
|
for (size_t i =0; i < estimatedScores.size(); ++i) {
|
|
sortHypsByVal.insert(make_pair(estimatedScores[i], connectedHyp[i]));
|
|
}
|
|
|
|
multimap<float, const Hypothesis*>::const_iterator it = --sortHypsByVal.end();
|
|
float bestScore = it->first;
|
|
//store best score as score of hyp 0
|
|
sortHypsByVal.insert(make_pair(bestScore, emptyHyp));
|
|
|
|
|
|
IFVERBOSE(3) {
|
|
for (multimap<float, const Hypothesis*>::const_iterator it = --sortHypsByVal.end(); it != --sortHypsByVal.begin(); --it) {
|
|
const Hypothesis* currHyp = it->second;
|
|
cerr << "Hyp " << currHyp->GetId() << ", estimated score: " << it->first << endl;
|
|
}
|
|
}
|
|
|
|
|
|
set <const Hypothesis*> survivingHyps; //store hyps that make the cut in this
|
|
|
|
VERBOSE(2, "BEST HYPO TARGET LENGTH : " << bestHypo->GetSize() << endl)
|
|
size_t numEdgesTotal = edgeDensity * bestHypo->GetSize(); //as per Shankar, aim for (density * target length of MAP solution) arcs
|
|
size_t numEdgesCreated = 0;
|
|
VERBOSE(2, "Target edge count: " << numEdgesTotal << endl);
|
|
|
|
float prevScore = -999999;
|
|
|
|
//now iterate over multimap
|
|
for (multimap<float, const Hypothesis*>::const_iterator it = --sortHypsByVal.end(); it != --sortHypsByVal.begin(); --it) {
|
|
float currEstimatedScore = it->first;
|
|
const Hypothesis* currHyp = it->second;
|
|
|
|
if (numEdgesCreated >= numEdgesTotal && prevScore > currEstimatedScore) //if this hyp has equal estimated score to previous, include its edges too
|
|
break;
|
|
|
|
prevScore = currEstimatedScore;
|
|
VERBOSE(3, "Num edges created : "<< numEdgesCreated << ", numEdges wanted " << numEdgesTotal << endl)
|
|
VERBOSE(3, "Considering hyp " << currHyp->GetId() << ", estimated score: " << it->first << endl)
|
|
|
|
survivingHyps.insert(currHyp); //CurrHyp made the cut
|
|
|
|
// is its best predecessor already included ?
|
|
if (survivingHyps.find(currHyp->GetPrevHypo()) != survivingHyps.end()) { //yes, then add an edge
|
|
vector <Edge>& edges = incomingEdges[currHyp];
|
|
Edge winningEdge(currHyp->GetPrevHypo(),currHyp,scale*(currHyp->GetScore() - currHyp->GetPrevHypo()->GetScore()),currHyp->GetCurrTargetPhrase());
|
|
edges.push_back(winningEdge);
|
|
++numEdgesCreated;
|
|
}
|
|
|
|
//let's try the arcs too
|
|
const ArcList *arcList = currHyp->GetArcList();
|
|
if (arcList != NULL) {
|
|
ArcList::const_iterator iterArcList;
|
|
for (iterArcList = arcList->begin() ; iterArcList != arcList->end() ; ++iterArcList) {
|
|
const Hypothesis *loserHypo = *iterArcList;
|
|
const Hypothesis* loserPrevHypo = loserHypo->GetPrevHypo();
|
|
if (survivingHyps.find(loserPrevHypo) != survivingHyps.end()) { //found it, add edge
|
|
double arcScore = loserHypo->GetScore() - loserPrevHypo->GetScore();
|
|
Edge losingEdge(loserPrevHypo, currHyp, arcScore*scale, loserHypo->GetCurrTargetPhrase());
|
|
vector <Edge>& edges = incomingEdges[currHyp];
|
|
edges.push_back(losingEdge);
|
|
++numEdgesCreated;
|
|
}
|
|
}
|
|
}
|
|
|
|
//Now if a successor node has already been visited, add an edge connecting the two
|
|
map < const Hypothesis*, set < const Hypothesis* > >::const_iterator outgoingIt = outgoingHyps.find(currHyp);
|
|
|
|
if (outgoingIt != outgoingHyps.end()) {//currHyp does have successors
|
|
const set<const Hypothesis*> & outHyps = outgoingIt->second; //the successors
|
|
for (set<const Hypothesis*>::const_iterator outHypIts = outHyps.begin(); outHypIts != outHyps.end(); ++outHypIts) {
|
|
const Hypothesis* succHyp = *outHypIts;
|
|
|
|
if (survivingHyps.find(succHyp) == survivingHyps.end()) //Have we encountered the successor yet?
|
|
continue; //No, move on to next
|
|
|
|
//Curr Hyp can be : a) the best predecessor of succ b) or an arc attached to succ
|
|
if (succHyp->GetPrevHypo() == currHyp) { //best predecessor
|
|
vector <Edge>& succEdges = incomingEdges[succHyp];
|
|
Edge succWinningEdge(currHyp, succHyp, scale*(succHyp->GetScore() - currHyp->GetScore()), succHyp->GetCurrTargetPhrase());
|
|
succEdges.push_back(succWinningEdge);
|
|
survivingHyps.insert(succHyp);
|
|
++numEdgesCreated;
|
|
}
|
|
|
|
//now, let's find an arc
|
|
const ArcList *arcList = succHyp->GetArcList();
|
|
if (arcList != NULL) {
|
|
ArcList::const_iterator iterArcList;
|
|
//QUESTION: What happens if there's more than one loserPrevHypo?
|
|
for (iterArcList = arcList->begin() ; iterArcList != arcList->end() ; ++iterArcList) {
|
|
const Hypothesis *loserHypo = *iterArcList;
|
|
const Hypothesis* loserPrevHypo = loserHypo->GetPrevHypo();
|
|
if (loserPrevHypo == currHyp) { //found it
|
|
vector <Edge>& succEdges = incomingEdges[succHyp];
|
|
double arcScore = loserHypo->GetScore() - currHyp->GetScore();
|
|
Edge losingEdge(currHyp, succHyp,scale* arcScore, loserHypo->GetCurrTargetPhrase());
|
|
succEdges.push_back(losingEdge);
|
|
++numEdgesCreated;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
connectedHyp.clear();
|
|
for (set <const Hypothesis*>::iterator it = survivingHyps.begin(); it != survivingHyps.end(); ++it) {
|
|
connectedHyp.push_back(*it);
|
|
}
|
|
|
|
VERBOSE(2, "Done! Num edges created : "<< numEdgesCreated << ", numEdges wanted " << numEdgesTotal << endl)
|
|
|
|
IFVERBOSE(3) {
|
|
cerr << "Surviving hyps: " ;
|
|
for (set <const Hypothesis*>::iterator it = survivingHyps.begin(); it != survivingHyps.end(); ++it) {
|
|
cerr << (*it)->GetId() << " ";
|
|
}
|
|
cerr << endl;
|
|
}
|
|
|
|
|
|
}
|
|
|
|
void calcNgramExpectations(Lattice & connectedHyp, map<const Hypothesis*, vector<Edge> >& incomingEdges,
|
|
map<Phrase, float>& finalNgramScores, bool posteriors)
|
|
{
|
|
|
|
sort(connectedHyp.begin(),connectedHyp.end(),ascendingCoverageCmp); //sort by increasing source word cov
|
|
|
|
/*cerr << "Lattice:" << endl;
|
|
for (Lattice::const_iterator i = connectedHyp.begin(); i != connectedHyp.end(); ++i) {
|
|
const Hypothesis* h = *i;
|
|
cerr << *h << endl;
|
|
const vector<Edge>& edges = incomingEdges[h];
|
|
for (size_t e = 0; e < edges.size(); ++e) {
|
|
cerr << edges[e];
|
|
}
|
|
}*/
|
|
|
|
map<const Hypothesis*, float> forwardScore;
|
|
forwardScore[connectedHyp[0]] = 0.0f; //forward score of hyp 0 is 1 (or 0 in logprob space)
|
|
set< const Hypothesis *> finalHyps; //store completed hyps
|
|
|
|
NgramScores ngramScores;//ngram scores for each hyp
|
|
|
|
for (size_t i = 1; i < connectedHyp.size(); ++i) {
|
|
const Hypothesis* currHyp = connectedHyp[i];
|
|
if (currHyp->GetWordsBitmap().IsComplete()) {
|
|
finalHyps.insert(currHyp);
|
|
}
|
|
|
|
VERBOSE(3, "Processing hyp: " << currHyp->GetId() << ", num words cov= " << currHyp->GetWordsBitmap().GetNumWordsCovered() << endl)
|
|
|
|
vector <Edge> & edges = incomingEdges[currHyp];
|
|
for (size_t e = 0; e < edges.size(); ++e) {
|
|
const Edge& edge = edges[e];
|
|
if (forwardScore.find(currHyp) == forwardScore.end()) {
|
|
forwardScore[currHyp] = forwardScore[edge.GetTailNode()] + edge.GetScore();
|
|
VERBOSE(3, "Fwd score["<<currHyp->GetId()<<"] = fwdScore["<<edge.GetTailNode()->GetId() << "] + edge Score: " << edge.GetScore() << endl)
|
|
} else {
|
|
forwardScore[currHyp] = log_sum(forwardScore[currHyp], forwardScore[edge.GetTailNode()] + edge.GetScore());
|
|
VERBOSE(3, "Fwd score["<<currHyp->GetId()<<"] += fwdScore["<<edge.GetTailNode()->GetId() << "] + edge Score: " << edge.GetScore() << endl)
|
|
}
|
|
}
|
|
|
|
//Process ngrams now
|
|
for (size_t j =0 ; j < edges.size(); ++j) {
|
|
Edge& edge = edges[j];
|
|
const NgramHistory & incomingPhrases = edge.GetNgrams(incomingEdges);
|
|
|
|
//let's first score ngrams introduced by this edge
|
|
for (NgramHistory::const_iterator it = incomingPhrases.begin(); it != incomingPhrases.end(); ++it) {
|
|
const Phrase& ngram = it->first;
|
|
const PathCounts& pathCounts = it->second;
|
|
VERBOSE(4, "Calculating score for: " << it->first << endl)
|
|
|
|
for (PathCounts::const_iterator pathCountIt = pathCounts.begin(); pathCountIt != pathCounts.end(); ++pathCountIt) {
|
|
//Score of an n-gram is forward score of head node of leftmost edge + all edge scores
|
|
const Path& path = pathCountIt->first;
|
|
//cerr << "path count for " << ngram << " is " << pathCountIt->second << endl;
|
|
float score = forwardScore[path[0]->GetTailNode()];
|
|
for (size_t i = 0; i < path.size(); ++i) {
|
|
score += path[i]->GetScore();
|
|
}
|
|
//if we're doing expectations, then the number of times the ngram
|
|
//appears on the path is relevant.
|
|
size_t count = posteriors ? 1 : pathCountIt->second;
|
|
for (size_t k = 0; k < count; ++k) {
|
|
ngramScores.addScore(currHyp,ngram,score);
|
|
}
|
|
}
|
|
}
|
|
|
|
//Now score ngrams that are just being propagated from the history
|
|
for (NgramScores::NodeScoreIterator it = ngramScores.nodeBegin(edge.GetTailNode());
|
|
it != ngramScores.nodeEnd(edge.GetTailNode()); ++it) {
|
|
const Phrase & currNgram = *(it->first);
|
|
float currNgramScore = it->second;
|
|
VERBOSE(4, "Calculating score for: " << currNgram << endl)
|
|
|
|
// For posteriors, don't double count ngrams
|
|
if (!posteriors || incomingPhrases.find(currNgram) == incomingPhrases.end()) {
|
|
float score = edge.GetScore() + currNgramScore;
|
|
ngramScores.addScore(currHyp,currNgram,score);
|
|
}
|
|
}
|
|
|
|
}
|
|
}
|
|
|
|
float Z = 9999999; //the total score of the lattice
|
|
|
|
//Done - Print out ngram posteriors for final hyps
|
|
for (set< const Hypothesis *>::iterator finalHyp = finalHyps.begin(); finalHyp != finalHyps.end(); ++finalHyp) {
|
|
const Hypothesis* hyp = *finalHyp;
|
|
|
|
for (NgramScores::NodeScoreIterator it = ngramScores.nodeBegin(hyp); it != ngramScores.nodeEnd(hyp); ++it) {
|
|
const Phrase& ngram = *(it->first);
|
|
if (finalNgramScores.find(ngram) == finalNgramScores.end()) {
|
|
finalNgramScores[ngram] = it->second;
|
|
} else {
|
|
finalNgramScores[ngram] = log_sum(it->second, finalNgramScores[ngram]);
|
|
}
|
|
}
|
|
|
|
if (Z == 9999999) {
|
|
Z = forwardScore[hyp];
|
|
} else {
|
|
Z = log_sum(Z, forwardScore[hyp]);
|
|
}
|
|
}
|
|
|
|
//Z *= scale; //scale the score
|
|
|
|
for (map<Phrase, float>::iterator finalScoresIt = finalNgramScores.begin(); finalScoresIt != finalNgramScores.end(); ++finalScoresIt) {
|
|
finalScoresIt->second = finalScoresIt->second - Z;
|
|
IFVERBOSE(2) {
|
|
VERBOSE(2,finalScoresIt->first << " [" << finalScoresIt->second << "]" << endl);
|
|
}
|
|
}
|
|
|
|
}
|
|
|
|
const NgramHistory& Edge::GetNgrams(map<const Hypothesis*, vector<Edge> > & incomingEdges)
|
|
{
|
|
|
|
if (m_ngrams.size() > 0)
|
|
return m_ngrams;
|
|
|
|
const Phrase& currPhrase = GetWords();
|
|
//Extract the n-grams local to this edge
|
|
for (size_t start = 0; start < currPhrase.GetSize(); ++start) {
|
|
for (size_t end = start; end < start + bleu_order; ++end) {
|
|
if (end < currPhrase.GetSize()) {
|
|
Phrase edgeNgram(end-start+1);
|
|
for (size_t index = start; index <= end; ++index) {
|
|
edgeNgram.AddWord(currPhrase.GetWord(index));
|
|
}
|
|
//cout << "Inserting Phrase : " << edgeNgram << endl;
|
|
vector<const Edge*> edgeHistory;
|
|
edgeHistory.push_back(this);
|
|
storeNgramHistory(edgeNgram, edgeHistory);
|
|
} else {
|
|
break;
|
|
}
|
|
}
|
|
}
|
|
|
|
map<const Hypothesis*, vector<Edge> >::iterator it = incomingEdges.find(m_tailNode);
|
|
if (it != incomingEdges.end()) { //node has incoming edges
|
|
vector<Edge> & inEdges = it->second;
|
|
|
|
for (vector<Edge>::iterator edge = inEdges.begin(); edge != inEdges.end(); ++edge) {//add the ngrams straddling prev and curr edge
|
|
const NgramHistory & edgeIncomingNgrams = edge->GetNgrams(incomingEdges);
|
|
for (NgramHistory::const_iterator edgeInNgramHist = edgeIncomingNgrams.begin(); edgeInNgramHist != edgeIncomingNgrams.end(); ++edgeInNgramHist) {
|
|
const Phrase& edgeIncomingNgram = edgeInNgramHist->first;
|
|
const PathCounts & edgeIncomingNgramPaths = edgeInNgramHist->second;
|
|
size_t back = min(edgeIncomingNgram.GetSize(), edge->GetWordsSize());
|
|
const Phrase& edgeWords = edge->GetWords();
|
|
IFVERBOSE(3) {
|
|
cerr << "Edge: "<< *edge <<endl;
|
|
cerr << "edgeWords: " << edgeWords << endl;
|
|
cerr << "edgeInNgram: " << edgeIncomingNgram << endl;
|
|
}
|
|
|
|
Phrase edgeSuffix(ARRAY_SIZE_INCR);
|
|
Phrase ngramSuffix(ARRAY_SIZE_INCR);
|
|
GetPhraseSuffix(edgeWords,back,edgeSuffix);
|
|
GetPhraseSuffix(edgeIncomingNgram,back,ngramSuffix);
|
|
|
|
if (ngramSuffix == edgeSuffix) { //we've got the suffix of previous edge
|
|
size_t edgeInNgramSize = edgeIncomingNgram.GetSize();
|
|
|
|
for (size_t i = 0; i < GetWordsSize() && i + edgeInNgramSize < bleu_order ; ++i) {
|
|
Phrase newNgram(edgeIncomingNgram);
|
|
for (size_t j = 0; j <= i ; ++j) {
|
|
newNgram.AddWord(GetWords().GetWord(j));
|
|
}
|
|
VERBOSE(3, "Inserting New Phrase : " << newNgram << endl)
|
|
|
|
for (PathCounts::const_iterator pathIt = edgeIncomingNgramPaths.begin(); pathIt != edgeIncomingNgramPaths.end(); ++pathIt) {
|
|
Path newNgramPath = pathIt->first;
|
|
newNgramPath.push_back(this);
|
|
storeNgramHistory(newNgram, newNgramPath, pathIt->second);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
return m_ngrams;
|
|
}
|
|
|
|
//Add the last lastN words of origPhrase to targetPhrase
|
|
void Edge::GetPhraseSuffix(const Phrase& origPhrase, size_t lastN, Phrase& targetPhrase) const
|
|
{
|
|
size_t origSize = origPhrase.GetSize();
|
|
size_t startIndex = origSize - lastN;
|
|
for (size_t index = startIndex; index < origPhrase.GetSize(); ++index) {
|
|
targetPhrase.AddWord(origPhrase.GetWord(index));
|
|
}
|
|
}
|
|
|
|
bool Edge::operator< (const Edge& compare ) const
|
|
{
|
|
if (m_headNode->GetId() < compare.m_headNode->GetId())
|
|
return true;
|
|
if (compare.m_headNode->GetId() < m_headNode->GetId())
|
|
return false;
|
|
if (m_tailNode->GetId() < compare.m_tailNode->GetId())
|
|
return true;
|
|
if (compare.m_tailNode->GetId() < m_tailNode->GetId())
|
|
return false;
|
|
return GetScore() < compare.GetScore();
|
|
}
|
|
|
|
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;
|
|
return out;
|
|
}
|
|
|
|
bool ascendingCoverageCmp(const Hypothesis* a, const Hypothesis* b)
|
|
{
|
|
return (a->GetWordsBitmap().GetNumWordsCovered()
|
|
<
|
|
b->GetWordsBitmap().GetNumWordsCovered());
|
|
}
|
|
|
|
void getLatticeMBRNBest(const Manager& manager, const TrellisPathList& nBestList,
|
|
vector<LatticeMBRSolution>& solutions, size_t n)
|
|
{
|
|
std::map < int, bool > connected;
|
|
std::vector< const Hypothesis *> connectedList;
|
|
map<Phrase, float> ngramPosteriors;
|
|
std::map < const Hypothesis*, set <const Hypothesis*> > outgoingHyps;
|
|
map<const Hypothesis*, vector<Edge> > incomingEdges;
|
|
vector< float> estimatedScores;
|
|
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 = 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) {
|
|
mbrThetas.push_back(mbrThetas[i-1] / r);
|
|
}
|
|
}
|
|
IFVERBOSE(2) {
|
|
VERBOSE(2,"Thetas: ");
|
|
for (size_t i = 0; i < mbrThetas.size(); ++i) {
|
|
VERBOSE(2,mbrThetas[i] << " ");
|
|
}
|
|
VERBOSE(2,endl);
|
|
}
|
|
TrellisPathList::const_iterator iter;
|
|
size_t ctr = 0;
|
|
LatticeMBRSolutionComparator comparator;
|
|
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);
|
|
sort(solutions.begin(), solutions.end(), comparator);
|
|
while (solutions.size() > n) {
|
|
solutions.pop_back();
|
|
}
|
|
}
|
|
VERBOSE(2,"LMBR Score: " << solutions[0].GetScore() << endl);
|
|
}
|
|
|
|
vector<Word> doLatticeMBR(const Manager& manager, const TrellisPathList& nBestList)
|
|
{
|
|
|
|
vector<LatticeMBRSolution> solutions;
|
|
getLatticeMBRNBest(manager, nBestList, solutions,1);
|
|
return solutions.at(0).GetWords();
|
|
}
|
|
|
|
const TrellisPath doConsensusDecoding(const Manager& manager, const TrellisPathList& nBestList)
|
|
{
|
|
static const int BLEU_ORDER = 4;
|
|
static const float SMOOTH = 1;
|
|
|
|
//calculate the ngram expectations
|
|
const StaticData& staticData = StaticData::Instance();
|
|
std::map < int, bool > connected;
|
|
std::vector< const Hypothesis *> connectedList;
|
|
map<Phrase, float> ngramExpectations;
|
|
std::map < const Hypothesis*, set <const Hypothesis*> > outgoingHyps;
|
|
map<const Hypothesis*, vector<Edge> > incomingEdges;
|
|
vector< float> estimatedScores;
|
|
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, ngramExpectations,false);
|
|
|
|
//expected length is sum of expected unigram counts
|
|
//cerr << "Thread " << pthread_self() << " Ngram expectations size: " << ngramExpectations.size() << endl;
|
|
float ref_length = 0.0f;
|
|
for (map<Phrase,float>::const_iterator ref_iter = ngramExpectations.begin();
|
|
ref_iter != ngramExpectations.end(); ++ref_iter) {
|
|
//cerr << "Ngram: " << ref_iter->first << " score: " <<
|
|
// ref_iter->second << endl;
|
|
if (ref_iter->first.GetSize() == 1) {
|
|
ref_length += exp(ref_iter->second);
|
|
// cerr << "Expected for " << ref_iter->first << " is " << exp(ref_iter->second) << endl;
|
|
}
|
|
}
|
|
|
|
VERBOSE(2,"REF Length: " << ref_length << endl);
|
|
|
|
//use the ngram expectations to rescore the nbest list.
|
|
TrellisPathList::const_iterator iter;
|
|
TrellisPathList::const_iterator best = nBestList.end();
|
|
float bestScore = -100000;
|
|
//cerr << "nbest list size: " << nBestList.GetSize() << endl;
|
|
for (iter = nBestList.begin() ; iter != nBestList.end() ; ++iter) {
|
|
const TrellisPath &path = **iter;
|
|
vector<Word> words;
|
|
map<Phrase,int> ngrams;
|
|
GetOutputWords(path,words);
|
|
/*for (size_t i = 0; i < words.size(); ++i) {
|
|
cerr << words[i].GetFactor(0)->GetString() << " ";
|
|
}
|
|
cerr << endl;
|
|
*/
|
|
extract_ngrams(words,ngrams);
|
|
|
|
vector<float> comps(2*BLEU_ORDER+1);
|
|
float logbleu = 0.0;
|
|
float brevity = 0.0;
|
|
int hyp_length = words.size();
|
|
for (int i = 0; i < BLEU_ORDER; ++i) {
|
|
comps[2*i] = 0.0;
|
|
comps[2*i+1] = max(hyp_length-i,0);
|
|
}
|
|
|
|
for (map<Phrase,int>::const_iterator hyp_iter = ngrams.begin();
|
|
hyp_iter != ngrams.end(); ++hyp_iter) {
|
|
map<Phrase,float>::const_iterator ref_iter = ngramExpectations.find(hyp_iter->first);
|
|
if (ref_iter != ngramExpectations.end()) {
|
|
comps[2*(hyp_iter->first.GetSize()-1)] += min(exp(ref_iter->second), (float)(hyp_iter->second));
|
|
}
|
|
|
|
}
|
|
comps[comps.size()-1] = ref_length;
|
|
/*for (size_t i = 0; i < comps.size(); ++i) {
|
|
cerr << comps[i] << " ";
|
|
}
|
|
cerr << endl;
|
|
*/
|
|
|
|
float score = 0.0f;
|
|
if (comps[0] != 0) {
|
|
for (int i=0; i<BLEU_ORDER; i++) {
|
|
if ( i > 0 ) {
|
|
logbleu += log((float)comps[2*i]+SMOOTH)-log((float)comps[2*i+1]+SMOOTH);
|
|
} else {
|
|
logbleu += log((float)comps[2*i])-log((float)comps[2*i+1]);
|
|
}
|
|
}
|
|
logbleu /= BLEU_ORDER;
|
|
brevity = 1.0-(float)comps[comps.size()-1]/comps[1]; // comps[comps_n-1] is the ref length, comps[1] is the test length
|
|
if (brevity < 0.0) {
|
|
logbleu += brevity;
|
|
}
|
|
score = exp(logbleu);
|
|
}
|
|
|
|
//cerr << "score: " << score << " bestScore: " << bestScore << endl;
|
|
if (score > bestScore) {
|
|
bestScore = score;
|
|
best = iter;
|
|
VERBOSE(2,"NEW BEST: " << score << endl);
|
|
//for (size_t i = 0; i < comps.size(); ++i) {
|
|
// cerr << comps[i] << " ";
|
|
//}
|
|
//cerr << endl;
|
|
}
|
|
}
|
|
|
|
assert (best != nBestList.end());
|
|
return **best;
|
|
//vector<Word> bestWords;
|
|
//GetOutputWords(**best,bestWords);
|
|
//return bestWords;
|
|
}
|
|
|
|
}
|
|
|
|
|