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#include "score.h"
namespace dtrain
{
/******************************************************************************
* NGRAMS
*
*
* make_ngrams
*
*/
typedef map<vector<WordID>, size_t> Ngrams;
Ngrams
make_ngrams( vector<WordID>& s, size_t N )
{
Ngrams ngrams;
vector<WordID> ng;
for ( size_t i = 0; i < s.size(); i++ ) {
ng.clear();
for ( size_t j = i; j < min( i+N, s.size() ); j++ ) {
ng.push_back( s[j] );
ngrams[ng]++;
}
}
return ngrams;
}
/*
* ngram_matches
*
*/
NgramCounts
make_ngram_counts( vector<WordID> hyp, vector<WordID> ref, size_t N )
{
Ngrams hyp_ngrams = make_ngrams( hyp, N );
Ngrams ref_ngrams = make_ngrams( ref, N );
NgramCounts counts( N );
Ngrams::iterator it;
Ngrams::iterator ti;
for ( it = hyp_ngrams.begin(); it != hyp_ngrams.end(); it++ ) {
ti = ref_ngrams.find( it->first );
if ( ti != ref_ngrams.end() ) {
counts.add( it->second, ti->second, it->first.size() - 1 );
} else {
counts.add( it->second, 0, it->first.size() - 1 );
}
}
return counts;
}
/******************************************************************************
* SCORERS
*
*
* brevity_penaly
*
*/
double
brevity_penaly( const size_t hyp_len, const size_t ref_len )
{
if ( hyp_len > ref_len ) return 1;
return exp( 1 - (double)ref_len/(double)hyp_len );
}
/*
* bleu
* as in "BLEU: a Method for Automatic Evaluation of Machine Translation" (Papineni et al. '02)
* page TODO
* 0 if for N one of the counts = 0
*/
double
bleu( NgramCounts& counts, const size_t hyp_len, const size_t ref_len,
size_t N, vector<float> weights )
{
if ( hyp_len == 0 || ref_len == 0 ) return 0;
if ( ref_len < N ) N = ref_len;
float N_ = (float)N;
if ( weights.empty() )
{
for ( size_t i = 0; i < N; i++ ) weights.push_back( 1/N_ );
}
double sum = 0;
for ( size_t i = 0; i < N; i++ ) {
if ( counts.clipped[i] == 0 || counts.sum[i] == 0 ) return 0;
sum += weights[i] * log( (double)counts.clipped[i] / (double)counts.sum[i] );
}
return brevity_penaly( hyp_len, ref_len ) * exp( sum );
}
/*
* stupid_bleu
* as in "ORANGE: a Method for Evaluating Automatic Evaluation Metrics for Machine Translation (Lin & Och '04)
* page TODO
* 0 iff no 1gram match
*/
double
stupid_bleu( NgramCounts& counts, const size_t hyp_len, const size_t ref_len,
size_t N, vector<float> weights )
{
if ( hyp_len == 0 || ref_len == 0 ) return 0;
if ( ref_len < N ) N = ref_len;
float N_ = (float)N;
if ( weights.empty() )
{
for ( size_t i = 0; i < N; i++ ) weights.push_back( 1/N_ );
}
double sum = 0;
float add = 0;
for ( size_t i = 0; i < N; i++ ) {
if ( i == 1 ) add = 1;
sum += weights[i] * log( ((double)counts.clipped[i] + add) / ((double)counts.sum[i] + add) );
}
return brevity_penaly( hyp_len, ref_len ) * exp( sum );
}
/*
* smooth_bleu
* as in "An End-to-End Discriminative Approach to Machine Translation" (Liang et al. '06)
* page TODO
* max. 0.9375
*/
double
smooth_bleu( NgramCounts& counts, const size_t hyp_len, const size_t ref_len,
const size_t N, vector<float> weights )
{
if ( hyp_len == 0 || ref_len == 0 ) return 0;
float N_ = (float)N;
if ( weights.empty() )
{
for ( size_t i = 0; i < N; i++ ) weights.push_back( 1/N_ );
}
double sum = 0;
float j = 1;
for ( size_t i = 0; i < N; i++ ) {
if ( counts.clipped[i] == 0 || counts.sum[i] == 0) continue;
sum += exp((weights[i] * log((double)counts.clipped[i]/(double)counts.sum[i]))) / pow( 2, N_-j+1 );
j++;
}
return brevity_penaly( hyp_len, ref_len ) * sum;
}
/*
* approx_bleu
* as in "Online Large-Margin Training for Statistical Machine Translation" (Watanabe et al. '07)
* CHIANG, RESNIK, synt struct features
* .9*
* page TODO
*
*/
double
approx_bleu( NgramCounts& counts, const size_t hyp_len, const size_t ref_len,
const size_t N, vector<float> weights )
{
return bleu( counts, hyp_len, ref_len, N, weights );
}
} // namespace
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