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Diffstat (limited to 'moses-cmd/LatticeMBR.cpp')
-rw-r--r-- | moses-cmd/LatticeMBR.cpp | 669 |
1 files changed, 0 insertions, 669 deletions
diff --git a/moses-cmd/LatticeMBR.cpp b/moses-cmd/LatticeMBR.cpp deleted file mode 100644 index 148b44743..000000000 --- a/moses-cmd/LatticeMBR.cpp +++ /dev/null @@ -1,669 +0,0 @@ -/* - * 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; -using namespace Moses; - -namespace MosesCmd -{ - -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(Manager& manager, TrellisPathList& nBestList, - vector<LatticeMBRSolution>& solutions, size_t n) -{ - const StaticData& staticData = StaticData::Instance(); - 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); - pruneLatticeFB(connectedList, outgoingHyps, incomingEdges, estimatedScores, manager.GetBestHypothesis(), staticData.GetLatticeMBRPruningFactor(),staticData.GetMBRScale()); - 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 - 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(Manager& manager, TrellisPathList& nBestList) -{ - - vector<LatticeMBRSolution> solutions; - getLatticeMBRNBest(manager, nBestList, solutions,1); - return solutions.at(0).GetWords(); -} - -const TrellisPath doConsensusDecoding(Manager& manager, 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); - pruneLatticeFB(connectedList, outgoingHyps, incomingEdges, estimatedScores, manager.GetBestHypothesis(), staticData.GetLatticeMBRPruningFactor(),staticData.GetMBRScale()); - 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; -} - -} - - |