mirror of
https://github.com/moses-smt/mosesdecoder.git
synced 2024-08-16 23:10:31 +03:00
200 lines
6.8 KiB
C++
200 lines
6.8 KiB
C++
#include <fstream>
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#include "GlobalLexicalModel.h"
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#include "moses/StaticData.h"
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#include "moses/InputFileStream.h"
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#include "moses/TranslationOption.h"
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#include "moses/TranslationTask.h"
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#include "moses/FactorCollection.h"
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#include "util/exception.hh"
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using namespace std;
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namespace Moses
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{
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GlobalLexicalModel::GlobalLexicalModel(const std::string &line)
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: StatelessFeatureFunction(1, line)
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{
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std::cerr << "Creating global lexical model...\n";
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ReadParameters();
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// define bias word
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FactorCollection &factorCollection = FactorCollection::Instance();
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m_bias = new Word();
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const Factor* factor = factorCollection.AddFactor( Input, m_inputFactorsVec[0], "**BIAS**" );
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m_bias->SetFactor( m_inputFactorsVec[0], factor );
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}
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void GlobalLexicalModel::SetParameter(const std::string& key, const std::string& value)
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{
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if (key == "path") {
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m_filePath = value;
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} else if (key == "input-factor") {
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m_inputFactorsVec = Tokenize<FactorType>(value,",");
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} else if (key == "output-factor") {
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m_outputFactorsVec = Tokenize<FactorType>(value,",");
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} else {
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StatelessFeatureFunction::SetParameter(key, value);
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}
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}
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GlobalLexicalModel::~GlobalLexicalModel()
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{
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// delete words in the hash data structure
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DoubleHash::const_iterator iter;
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for(iter = m_hash.begin(); iter != m_hash.end(); iter++ ) {
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boost::unordered_map< const Word*, float, UnorderedComparer<Word>, UnorderedComparer<Word> >::const_iterator iter2;
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for(iter2 = iter->second.begin(); iter2 != iter->second.end(); iter2++ ) {
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delete iter2->first; // delete input word
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}
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delete iter->first; // delete output word
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}
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}
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void GlobalLexicalModel::Load(AllOptions::ptr const& opts)
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{
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m_options = opts;
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FactorCollection &factorCollection = FactorCollection::Instance();
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const std::string& oFactorDelimiter = opts->output.factor_delimiter;
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const std::string& iFactorDelimiter = opts->input.factor_delimiter;
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VERBOSE(2, "Loading global lexical model from file " << m_filePath << endl);
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m_inputFactors = FactorMask(m_inputFactorsVec);
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m_outputFactors = FactorMask(m_outputFactorsVec);
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InputFileStream inFile(m_filePath);
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// reading in data one line at a time
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size_t lineNum = 0;
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string line;
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while(getline(inFile, line)) {
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++lineNum;
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vector<string> token = Tokenize<string>(line, " ");
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if (token.size() != 3) { // format checking
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UTIL_THROW2("Syntax error at " << m_filePath << ":" << lineNum << ":" << line);
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}
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// create the output word
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Word *outWord = new Word();
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vector<string> factorString = Tokenize( token[0], oFactorDelimiter );
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for (size_t i=0 ; i < m_outputFactorsVec.size() ; i++) {
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const FactorDirection& direction = Output;
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const FactorType& factorType = m_outputFactorsVec[i];
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const Factor* factor
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= factorCollection.AddFactor( direction, factorType, factorString[i] );
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outWord->SetFactor( factorType, factor );
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}
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// create the input word
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Word *inWord = new Word();
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factorString = Tokenize( token[1], iFactorDelimiter );
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for (size_t i=0 ; i < m_inputFactorsVec.size() ; i++) {
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const FactorDirection& direction = Input;
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const FactorType& factorType = m_inputFactorsVec[i];
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const Factor* factor
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= factorCollection.AddFactor( direction, factorType, factorString[i] );
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inWord->SetFactor( factorType, factor );
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}
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// maximum entropy feature score
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float score = Scan<float>(token[2]);
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// std::cerr << "storing word " << *outWord << " " << *inWord << " " << score << endl;
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// store feature in hash
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DoubleHash::iterator keyOutWord = m_hash.find( outWord );
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if( keyOutWord == m_hash.end() ) {
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m_hash[outWord][inWord] = score;
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} else { // already have hash for outword, delete the word to avoid leaks
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(keyOutWord->second)[inWord] = score;
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delete outWord;
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}
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}
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}
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void GlobalLexicalModel::InitializeForInput(ttasksptr const& ttask)
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{
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UTIL_THROW_IF2(ttask->GetSource()->GetType() != SentenceInput,
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"GlobalLexicalModel works only with sentence input.");
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Sentence const* s = reinterpret_cast<Sentence const*>(ttask->GetSource().get());
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m_local.reset(new ThreadLocalStorage);
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m_local->input = s;
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}
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float GlobalLexicalModel::ScorePhrase( const TargetPhrase& targetPhrase ) const
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{
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const Sentence& input = *(m_local->input);
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float score = 0;
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for(size_t targetIndex = 0; targetIndex < targetPhrase.GetSize(); targetIndex++ ) {
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float sum = 0;
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const Word& targetWord = targetPhrase.GetWord( targetIndex );
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VERBOSE(2,"glm " << targetWord << ": ");
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const DoubleHash::const_iterator targetWordHash = m_hash.find( &targetWord );
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if( targetWordHash != m_hash.end() ) {
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SingleHash::const_iterator inputWordHash = targetWordHash->second.find( m_bias );
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if( inputWordHash != targetWordHash->second.end() ) {
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VERBOSE(2,"*BIAS* " << inputWordHash->second);
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sum += inputWordHash->second;
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}
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boost::unordered_set< const Word*, UnorderedComparer<Word>, UnorderedComparer<Word> > alreadyScored; // do not score a word twice
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for(size_t inputIndex = 0; inputIndex < input.GetSize(); inputIndex++ ) {
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const Word& inputWord = input.GetWord( inputIndex );
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if ( alreadyScored.find( &inputWord ) == alreadyScored.end() ) {
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SingleHash::const_iterator inputWordHash = targetWordHash->second.find( &inputWord );
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if( inputWordHash != targetWordHash->second.end() ) {
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VERBOSE(2," " << inputWord << " " << inputWordHash->second);
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sum += inputWordHash->second;
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}
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alreadyScored.insert( &inputWord );
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}
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}
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}
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// Hal Daume says: 1/( 1 + exp [ - sum_i w_i * f_i ] )
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VERBOSE(2," p=" << FloorScore( log(1/(1+exp(-sum))) ) << endl);
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score += FloorScore( log(1/(1+exp(-sum))) );
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}
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return score;
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}
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float GlobalLexicalModel::GetFromCacheOrScorePhrase( const TargetPhrase& targetPhrase ) const
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{
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LexiconCache& m_cache = m_local->cache;
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const LexiconCache::const_iterator query = m_cache.find( &targetPhrase );
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if ( query != m_cache.end() ) {
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return query->second;
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}
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float score = ScorePhrase( targetPhrase );
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m_cache.insert( pair<const TargetPhrase*, float>(&targetPhrase, score) );
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//VERBOSE(2, "add to cache " << targetPhrase << ": " << score << endl);
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return score;
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}
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void GlobalLexicalModel::EvaluateWithSourceContext(const InputType &input
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, const InputPath &inputPath
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, const TargetPhrase &targetPhrase
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, const StackVec *stackVec
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, ScoreComponentCollection &scoreBreakdown
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, ScoreComponentCollection *estimatedScores) const
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{
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scoreBreakdown.PlusEquals( this, GetFromCacheOrScorePhrase(targetPhrase) );
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}
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bool GlobalLexicalModel::IsUseable(const FactorMask &mask) const
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{
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for (size_t i = 0; i < m_outputFactors.size(); ++i) {
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if (m_outputFactors[i]) {
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if (!mask[i]) {
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return false;
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}
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}
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}
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return true;
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}
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}
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