1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
|
#include <fstream>
#include "GlobalLexicalModel.h"
#include "StaticData.h"
#include "InputFileStream.h"
#include "TranslationOption.h"
#include "UserMessage.h"
using namespace std;
namespace Moses
{
GlobalLexicalModel::GlobalLexicalModel(const string &filePath,
const vector< FactorType >& inFactors,
const vector< FactorType >& outFactors)
: StatelessFeatureFunction("GlobalLexicalModel",1)
{
std::cerr << "Creating global lexical model...\n";
// load model
LoadData( filePath, inFactors, outFactors );
// define bias word
FactorCollection &factorCollection = FactorCollection::Instance();
m_bias = new Word();
const Factor* factor = factorCollection.AddFactor( Input, inFactors[0], "**BIAS**" );
m_bias->SetFactor( inFactors[0], factor );
}
GlobalLexicalModel::~GlobalLexicalModel()
{
// delete words in the hash data structure
DoubleHash::const_iterator iter;
for(iter = m_hash.begin(); iter != m_hash.end(); iter++ ) {
map< const Word*, float, WordComparer >::const_iterator iter2;
for(iter2 = iter->second.begin(); iter2 != iter->second.end(); iter2++ ) {
delete iter2->first; // delete input word
}
delete iter->first; // delete output word
}
}
void GlobalLexicalModel::LoadData(const string &filePath,
const vector< FactorType >& inFactors,
const vector< FactorType >& outFactors)
{
FactorCollection &factorCollection = FactorCollection::Instance();
const std::string& factorDelimiter = StaticData::Instance().GetFactorDelimiter();
VERBOSE(2, "Loading global lexical model from file " << filePath << endl);
m_inputFactors = FactorMask(inFactors);
m_outputFactors = FactorMask(outFactors);
InputFileStream inFile(filePath);
// reading in data one line at a time
size_t lineNum = 0;
string line;
while(getline(inFile, line)) {
++lineNum;
vector<string> token = Tokenize<string>(line, " ");
if (token.size() != 3) { // format checking
stringstream errorMessage;
errorMessage << "Syntax error at " << filePath << ":" << lineNum << endl << line << endl;
UserMessage::Add(errorMessage.str());
abort();
}
// create the output word
Word *outWord = new Word();
vector<string> factorString = Tokenize( token[0], factorDelimiter );
for (size_t i=0 ; i < outFactors.size() ; i++) {
const FactorDirection& direction = Output;
const FactorType& factorType = outFactors[i];
const Factor* factor = factorCollection.AddFactor( direction, factorType, factorString[i] );
outWord->SetFactor( factorType, factor );
}
// create the input word
Word *inWord = new Word();
factorString = Tokenize( token[1], factorDelimiter );
for (size_t i=0 ; i < inFactors.size() ; i++) {
const FactorDirection& direction = Input;
const FactorType& factorType = inFactors[i];
const Factor* factor = factorCollection.AddFactor( direction, factorType, factorString[i] );
inWord->SetFactor( factorType, factor );
}
// maximum entropy feature score
float score = Scan<float>(token[2]);
// std::cerr << "storing word " << *outWord << " " << *inWord << " " << score << endl;
// store feature in hash
DoubleHash::iterator keyOutWord = m_hash.find( outWord );
if( keyOutWord == m_hash.end() ) {
m_hash[outWord][inWord] = score;
} else { // already have hash for outword, delete the word to avoid leaks
(keyOutWord->second)[inWord] = score;
delete outWord;
}
}
}
void GlobalLexicalModel::InitializeForInput( Sentence const& in )
{
m_local.reset(new ThreadLocalStorage);
m_local->input = ∈
}
float GlobalLexicalModel::ScorePhrase( const TargetPhrase& targetPhrase ) const
{
const Sentence& input = *(m_local->input);
float score = 0;
for(size_t targetIndex = 0; targetIndex < targetPhrase.GetSize(); targetIndex++ ) {
float sum = 0;
const Word& targetWord = targetPhrase.GetWord( targetIndex );
VERBOSE(2,"glm " << targetWord << ": ");
const DoubleHash::const_iterator targetWordHash = m_hash.find( &targetWord );
if( targetWordHash != m_hash.end() ) {
SingleHash::const_iterator inputWordHash = targetWordHash->second.find( m_bias );
if( inputWordHash != targetWordHash->second.end() ) {
VERBOSE(2,"*BIAS* " << inputWordHash->second);
sum += inputWordHash->second;
}
set< const Word*, WordComparer > alreadyScored; // do not score a word twice
for(size_t inputIndex = 0; inputIndex < input.GetSize(); inputIndex++ ) {
const Word& inputWord = input.GetWord( inputIndex );
if ( alreadyScored.find( &inputWord ) == alreadyScored.end() ) {
SingleHash::const_iterator inputWordHash = targetWordHash->second.find( &inputWord );
if( inputWordHash != targetWordHash->second.end() ) {
VERBOSE(2," " << inputWord << " " << inputWordHash->second);
sum += inputWordHash->second;
}
alreadyScored.insert( &inputWord );
}
}
}
// Hal Daume says: 1/( 1 + exp [ - sum_i w_i * f_i ] )
VERBOSE(2," p=" << FloorScore( log(1/(1+exp(-sum))) ) << endl);
score += FloorScore( log(1/(1+exp(-sum))) );
}
return score;
}
float GlobalLexicalModel::GetFromCacheOrScorePhrase( const TargetPhrase& targetPhrase ) const
{
LexiconCache& m_cache = m_local->cache;
const LexiconCache::const_iterator query = m_cache.find( &targetPhrase );
if ( query != m_cache.end() ) {
return query->second;
}
float score = ScorePhrase( targetPhrase );
m_cache.insert( pair<const TargetPhrase*, float>(&targetPhrase, score) );
//VERBOSE(2, "add to cache " << targetPhrase << ": " << score << endl);
return score;
}
void GlobalLexicalModel::Evaluate
(const PhraseBasedFeatureContext& context,
ScoreComponentCollection* accumulator) const
{
accumulator->PlusEquals( this,
GetFromCacheOrScorePhrase(context.GetTargetPhrase()) );
}
}
|
