moses-smt/mosesdecoder
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dfd3a53e9e
mosesdecoder/mert/ForestRescore.cpp
Barry Haddow a299bb1943 Fix hypergraph pruning
2014-08-08 13:45:34 +01:00

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/***********************************************************************
Moses - factored phrase-based language decoder
Copyright (C) 2014- 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 <cmath>
#include <limits>
#include <list>
#include <boost/unordered_set.hpp>
#include "util/file_piece.hh"
#include "util/tokenize_piece.hh"
#include "BleuScorer.h"
#include "ForestRescore.h"
using namespace std;
namespace MosesTuning {
std::ostream& operator<<(std::ostream& out, const WordVec& wordVec) {
out << "[";
for (size_t i = 0; i < wordVec.size(); ++i) {
out << wordVec[i]->first;
if (i+1< wordVec.size()) out << " ";
}
out << "]";
return out;
}
void ReferenceSet::Load(const vector<string>& files, Vocab& vocab) {
for (size_t i = 0; i < files.size(); ++i) {
util::FilePiece fh(files[i].c_str());
size_t sentenceId = 0;
while(true) {
StringPiece line;
try {
line = fh.ReadLine();
} catch (util::EndOfFileException &e) {
break;
}
AddLine(sentenceId, line, vocab);
++sentenceId;
}
}
}
void ReferenceSet::AddLine(size_t sentenceId, const StringPiece& line, Vocab& vocab) {
//cerr << line << endl;
NgramCounter ngramCounts;
list<WordVec> openNgrams;
size_t length = 0;
//tokenize & count
for (util::TokenIter<util::SingleCharacter, true> j(line, util::SingleCharacter(' ')); j; ++j) {
const Vocab::Entry* nextTok = &(vocab.FindOrAdd(*j));
++length;
openNgrams.push_front(WordVec());
for (list<WordVec>::iterator k = openNgrams.begin(); k != openNgrams.end(); ++k) {
k->push_back(nextTok);
++ngramCounts[*k];
}
if (openNgrams.size() >= kBleuNgramOrder) openNgrams.pop_back();
}
//merge into overall ngram map
for (NgramCounter::const_iterator ni = ngramCounts.begin();
ni != ngramCounts.end(); ++ni) {
size_t count = ni->second;
//cerr << *ni << " " << count << endl;
if (ngramCounts_.size() <= sentenceId) ngramCounts_.resize(sentenceId+1);
NgramMap::iterator totalsIter = ngramCounts_[sentenceId].find(ni->first);
if (totalsIter == ngramCounts_[sentenceId].end()) {
ngramCounts_[sentenceId][ni->first] = pair<size_t,size_t>(count,count);
} else {
ngramCounts_[sentenceId][ni->first].first = max(count, ngramCounts_[sentenceId][ni->first].first); //clip
ngramCounts_[sentenceId][ni->first].second += count; //no clip
}
}
//length
if (lengths_.size() <= sentenceId) lengths_.resize(sentenceId+1);
//TODO - length strategy - this is MIN
if (!lengths_[sentenceId]) {
lengths_[sentenceId] = length;
} else {
lengths_[sentenceId] = min(length,lengths_[sentenceId]);
}
//cerr << endl;
}
size_t ReferenceSet::NgramMatches(size_t sentenceId, const WordVec& ngram, bool clip) const {
const NgramMap& ngramCounts = ngramCounts_.at(sentenceId);
NgramMap::const_iterator ngi = ngramCounts.find(ngram);
if (ngi == ngramCounts.end()) return 0;
return clip ? ngi->second.first : ngi->second.second;
}
VertexState::VertexState(): bleuStats(kBleuNgramOrder), targetLength(0) {}
void HgBleuScorer::UpdateMatches(const NgramCounter& counts, vector<FeatureStatsType>& bleuStats ) const {
for (NgramCounter::const_iterator ngi = counts.begin(); ngi != counts.end(); ++ngi) {
//cerr << "Checking: " << *ngi << " matches " << references_.NgramMatches(sentenceId_,*ngi,false) << endl;
size_t order = ngi->first.size();
size_t count = ngi->second;
bleuStats[(order-1)*2 + 1] += count;
bleuStats[(order-1) * 2] += min(count, references_.NgramMatches(sentenceId_,ngi->first,false));
}
}
size_t HgBleuScorer::GetTargetLength(const Edge& edge) const {
size_t targetLength = 0;
for (size_t i = 0; i < edge.Words().size(); ++i) {
const Vocab::Entry* word = edge.Words()[i];
if (word) ++targetLength;
}
for (size_t i = 0; i < edge.Children().size(); ++i) {
const VertexState& state = vertexStates_[edge.Children()[i]];
targetLength += state.targetLength;
}
return targetLength;
}
FeatureStatsType HgBleuScorer::Score(const Edge& edge, const Vertex& head, vector<FeatureStatsType>& bleuStats) {
NgramCounter ngramCounts;
size_t childId = 0;
size_t wordId = 0;
size_t contextId = 0; //position within left or right context
const VertexState* vertexState = NULL;
bool inLeftContext = false;
bool inRightContext = false;
list<WordVec> openNgrams;
const Vocab::Entry* currentWord = NULL;
while (wordId < edge.Words().size()) {
currentWord = edge.Words()[wordId];
if (currentWord != NULL) {
++wordId;
} else {
if (!inLeftContext && !inRightContext) {
//entering a vertex
assert(!vertexState);
vertexState = &(vertexStates_[edge.Children()[childId]]);
++childId;
if (vertexState->leftContext.size()) {
inLeftContext = true;
contextId = 0;
currentWord = vertexState->leftContext[contextId];
} else {
//empty context
vertexState = NULL;
++wordId;
continue;
}
} else {
//already in a vertex
++contextId;
if (inLeftContext && contextId < vertexState->leftContext.size()) {
//still in left context
currentWord = vertexState->leftContext[contextId];
} else if (inLeftContext) {
//at end of left context
if (vertexState->leftContext.size() == kBleuNgramOrder-1) {
//full size context, jump to right state
openNgrams.clear();
inLeftContext = false;
inRightContext = true;
contextId = 0;
currentWord = vertexState->rightContext[contextId];
} else {
//short context, just ignore right context
inLeftContext = false;
vertexState = NULL;
++wordId;
continue;
}
} else {
//in right context
if (contextId < vertexState->rightContext.size()) {
currentWord = vertexState->rightContext[contextId];
} else {
//leaving vertex
inRightContext = false;
vertexState = NULL;
++wordId;
continue;
}
}
}
}
assert(currentWord);
if (graph_.IsBoundary(currentWord)) continue;
openNgrams.push_front(WordVec());
openNgrams.front().reserve(kBleuNgramOrder);
for (list<WordVec>::iterator k = openNgrams.begin(); k != openNgrams.end(); ++k) {
k->push_back(currentWord);
//Only insert ngrams that cross boundaries
if (!vertexState || (inLeftContext && k->size() > contextId+1)) ++ngramCounts[*k];
}
if (openNgrams.size() >= kBleuNgramOrder) openNgrams.pop_back();
}
//Collect matches
//This edge
//cerr << "edge ngrams" << endl;
UpdateMatches(ngramCounts, bleuStats);
//Child vertexes
for (size_t i = 0; i < edge.Children().size(); ++i) {
//cerr << "vertex ngrams " << edge.Children()[i] << endl;
for (size_t j = 0; j < bleuStats.size(); ++j) {
bleuStats[j] += vertexStates_[edge.Children()[i]].bleuStats[j];
}
}
FeatureStatsType sourceLength = head.SourceCovered();
size_t referenceLength = references_.Length(sentenceId_);
FeatureStatsType effectiveReferenceLength =
sourceLength / totalSourceLength_ * referenceLength;
bleuStats[bleuStats.size()-1] = effectiveReferenceLength;
//backgroundBleu_[backgroundBleu_.size()-1] =
// backgroundRefLength_ * sourceLength / totalSourceLength_;
FeatureStatsType bleu = sentenceLevelBackgroundBleu(bleuStats, backgroundBleu_);
return bleu;
}
void HgBleuScorer::UpdateState(const Edge& winnerEdge, size_t vertexId, const vector<FeatureStatsType>& bleuStats) {
//TODO: Maybe more efficient to absorb into the Score() method
VertexState& vertexState = vertexStates_[vertexId];
//cerr << "Updating state for " << vertexId << endl;
//leftContext
int wi = 0;
const VertexState* childState = NULL;
int contexti = 0; //index within child context
int childi = 0;
while (vertexState.leftContext.size() < (kBleuNgramOrder-1)) {
if ((size_t)wi >= winnerEdge.Words().size()) break;
const Vocab::Entry* word = winnerEdge.Words()[wi];
if (word != NULL) {
vertexState.leftContext.push_back(word);
++wi;
} else {
if (childState == NULL) {
//start of child state
childState = &(vertexStates_[winnerEdge.Children()[childi++]]);
contexti = 0;
}
if ((size_t)contexti < childState->leftContext.size()) {
vertexState.leftContext.push_back(childState->leftContext[contexti++]);
} else {
//end of child context
childState = NULL;
++wi;
}
}
}
//rightContext
wi = winnerEdge.Words().size() - 1;
childState = NULL;
childi = winnerEdge.Children().size() - 1;
while (vertexState.rightContext.size() < (kBleuNgramOrder-1)) {
if (wi < 0) break;
const Vocab::Entry* word = winnerEdge.Words()[wi];
if (word != NULL) {
vertexState.rightContext.push_back(word);
--wi;
} else {
if (childState == NULL) {
//start (ie rhs) of child state
childState = &(vertexStates_[winnerEdge.Children()[childi--]]);
contexti = childState->rightContext.size()-1;
}
if (contexti >= 0) {
vertexState.rightContext.push_back(childState->rightContext[contexti--]);
} else {
//end (ie lhs) of child context
childState = NULL;
--wi;
}
}
}
reverse(vertexState.rightContext.begin(), vertexState.rightContext.end());
//length + counts
vertexState.targetLength = GetTargetLength(winnerEdge);
vertexState.bleuStats = bleuStats;
}
typedef pair<const Edge*,FeatureStatsType> BackPointer;
/**
* Recurse through back pointers
**/
static void GetBestHypothesis(size_t vertexId, const Graph& graph, const vector<BackPointer>& bps,
HgHypothesis* bestHypo) {
//cerr << "Expanding " << vertexId << " Score: " << bps[vertexId].second << endl;
//UTIL_THROW_IF(bps[vertexId].second == kMinScore+1, HypergraphException, "Landed at vertex " << vertexId << " which is a dead end");
if (!bps[vertexId].first) return;
const Edge* prevEdge = bps[vertexId].first;
bestHypo->featureVector += *(prevEdge->Features().get());
size_t childId = 0;
for (size_t i = 0; i < prevEdge->Words().size(); ++i) {
if (prevEdge->Words()[i] != NULL) {
bestHypo->text.push_back(prevEdge->Words()[i]);
} else {
size_t childVertexId = prevEdge->Children()[childId++];
HgHypothesis childHypo;
GetBestHypothesis(childVertexId,graph,bps,&childHypo);
bestHypo->text.insert(bestHypo->text.end(), childHypo.text.begin(), childHypo.text.end());
bestHypo->featureVector += childHypo.featureVector;
}
}
}
void Viterbi(const Graph& graph, const SparseVector& weights, float bleuWeight, const ReferenceSet& references , size_t sentenceId, const std::vector<FeatureStatsType>& backgroundBleu, HgHypothesis* bestHypo)
{
BackPointer init(NULL,kMinScore);
vector<BackPointer> backPointers(graph.VertexSize(),init);
HgBleuScorer bleuScorer(references, graph, sentenceId, backgroundBleu);
vector<FeatureStatsType> winnerStats(kBleuNgramOrder*2+1);
for (size_t vi = 0; vi < graph.VertexSize(); ++vi) {
//cerr << "vertex id " << vi << endl;
FeatureStatsType winnerScore = kMinScore;
const Vertex& vertex = graph.GetVertex(vi);
const vector<const Edge*>& incoming = vertex.GetIncoming();
if (!incoming.size()) {
//UTIL_THROW(HypergraphException, "Vertex " << vi << " has no incoming edges");
//If no incoming edges, vertex is a dead end
backPointers[vi].first = NULL;
backPointers[vi].second = kMinScore/2;
} else {
//cerr << "\nVertex: " << vi << endl;
for (size_t ei = 0; ei < incoming.size(); ++ei) {
//cerr << "edge id " << ei << endl;
FeatureStatsType incomingScore = incoming[ei]->GetScore(weights);
for (size_t i = 0; i < incoming[ei]->Children().size(); ++i) {
size_t childId = incoming[ei]->Children()[i];
UTIL_THROW_IF(backPointers[childId].second == kMinScore,
HypergraphException, "Graph was not topologically sorted. curr=" << vi << " prev=" << childId);
incomingScore += backPointers[childId].second;
}
vector<FeatureStatsType> bleuStats(kBleuNgramOrder*2+1);
// cerr << "Score: " << incomingScore << " Bleu: ";
// if (incomingScore > nonbleuscore) {nonbleuscore = incomingScore; nonbleuid = ei;}
FeatureStatsType totalScore = incomingScore;
if (bleuWeight) {
FeatureStatsType bleuScore = bleuScorer.Score(*(incoming[ei]), vertex, bleuStats);
if (isnan(bleuScore)) {
cerr << "WARN: bleu score undefined" << endl;
cerr << "\tVertex id : " << vi << endl;
cerr << "\tBleu stats : ";
for (size_t i = 0; i < bleuStats.size(); ++i) {
cerr << bleuStats[i] << ",";
}
cerr << endl;
bleuScore = 0;
}
//UTIL_THROW_IF(isnan(bleuScore), util::Exception, "Bleu score undefined, smoothing problem?");
totalScore += bleuWeight * bleuScore;
// cerr << bleuScore << " Total: " << incomingScore << endl << endl;
//cerr << "is " << incomingScore << " bs " << bleuScore << endl;
}
if (totalScore >= winnerScore) {
//We only store the feature score (not the bleu score) with the vertex,
//since the bleu score is always cumulative, ie from counts for the whole span.
winnerScore = totalScore;
backPointers[vi].first = incoming[ei];
backPointers[vi].second = incomingScore;
winnerStats = bleuStats;
}
}
//update with winner
//if (bleuWeight) {
//TODO: Not sure if we need this when computing max-model solution
bleuScorer.UpdateState(*(backPointers[vi].first), vi, winnerStats);
}
}
//expand back pointers
GetBestHypothesis(graph.VertexSize()-1, graph, backPointers, bestHypo);
//bleu stats and fv
//Need the actual (clipped) stats
//TODO: This repeats code in bleu scorer - factor out
bestHypo->bleuStats.resize(kBleuNgramOrder*2+1);
NgramCounter counts;
list<WordVec> openNgrams;
for (size_t i = 0; i < bestHypo->text.size(); ++i) {
const Vocab::Entry* entry = bestHypo->text[i];
if (graph.IsBoundary(entry)) continue;
openNgrams.push_front(WordVec());
for (list<WordVec>::iterator k = openNgrams.begin(); k != openNgrams.end(); ++k) {
k->push_back(entry);
++counts[*k];
}
if (openNgrams.size() >= kBleuNgramOrder) openNgrams.pop_back();
}
for (NgramCounter::const_iterator ngi = counts.begin(); ngi != counts.end(); ++ngi) {
size_t order = ngi->first.size();
size_t count = ngi->second;
bestHypo->bleuStats[(order-1)*2 + 1] += count;
bestHypo->bleuStats[(order-1) * 2] += min(count, references.NgramMatches(sentenceId,ngi->first,true));
}
bestHypo->bleuStats[kBleuNgramOrder*2] = references.Length(sentenceId);
}
};
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