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#include "score.h"
namespace dtrain
{
/*
* bleu
*
* as in "BLEU: a Method for Automatic Evaluation
* of Machine Translation"
* (Papineni et al. '02)
*
* NOTE: 0 if for one n \in {1..N} count is 0
*/
score_t
BleuScorer::Bleu(NgramCounts& counts, const unsigned hyp_len, const unsigned ref_len)
{
if (hyp_len == 0 || ref_len == 0) return 0.;
unsigned M = N_;
vector<score_t> v = w_;
if (ref_len < N_) {
M = ref_len;
for (unsigned i = 0; i < M; i++) v[i] = 1/((score_t)M);
}
score_t sum = 0;
for (unsigned i = 0; i < M; i++) {
if (counts.sum_[i] == 0 || counts.clipped_[i] == 0) return 0.;
sum += v[i] * log((score_t)counts.clipped_[i]/counts.sum_[i]);
}
return brevity_penalty(hyp_len, ref_len) * exp(sum);
}
score_t
BleuScorer::Score(vector<WordID>& hyp, vector<WordID>& ref,
const unsigned /*rank*/, const unsigned /*src_len*/)
{
unsigned hyp_len = hyp.size(), ref_len = ref.size();
if (hyp_len == 0 || ref_len == 0) return 0.;
NgramCounts counts = make_ngram_counts(hyp, ref, N_);
return Bleu(counts, hyp_len, ref_len);
}
/*
* 'stupid' bleu
*
* as in "ORANGE: a Method for Evaluating
* Automatic Evaluation Metrics
* for Machine Translation"
* (Lin & Och '04)
*
* NOTE: 0 iff no 1gram match
*/
score_t
StupidBleuScorer::Score(vector<WordID>& hyp, vector<WordID>& ref,
const unsigned /*rank*/, const unsigned /*src_len*/)
{
unsigned hyp_len = hyp.size(), ref_len = ref.size();
if (hyp_len == 0 || ref_len == 0) return 0.;
NgramCounts counts = make_ngram_counts(hyp, ref, N_);
unsigned M = N_;
vector<score_t> v = w_;
if (ref_len < N_) {
M = ref_len;
for (unsigned i = 0; i < M; i++) v[i] = 1/((score_t)M);
}
score_t sum = 0, add = 0;
for (unsigned i = 0; i < M; i++) {
if (i == 0 && (counts.sum_[i] == 0 || counts.clipped_[i] == 0)) return 0.;
if (i == 1) add = 1;
sum += v[i] * log(((score_t)counts.clipped_[i] + add)/((counts.sum_[i] + add)));
}
return brevity_penalty(hyp_len, ref_len) * exp(sum);
}
/*
* smooth bleu
*
* as in "An End-to-End Discriminative Approach
* to Machine Translation"
* (Liang et al. '06)
*
* NOTE: max is 0.9375
*/
score_t
SmoothBleuScorer::Score(vector<WordID>& hyp, vector<WordID>& ref,
const unsigned /*rank*/, const unsigned /*src_len*/)
{
unsigned hyp_len = hyp.size(), ref_len = ref.size();
if (hyp_len == 0 || ref_len == 0) return 0.;
NgramCounts counts = make_ngram_counts(hyp, ref, N_);
unsigned M = N_;
if (ref_len < N_) M = ref_len;
score_t sum = 0.;
vector<score_t> i_bleu;
for (unsigned i = 0; i < M; i++) i_bleu.push_back(0.);
for (unsigned i = 0; i < M; i++) {
if (counts.sum_[i] == 0 || counts.clipped_[i] == 0) {
break;
} else {
score_t i_ng = log((score_t)counts.clipped_[i]/counts.sum_[i]);
for (unsigned j = i; j < M; j++) {
i_bleu[j] += (1/((score_t)j+1)) * i_ng;
}
}
sum += exp(i_bleu[i])/(pow(2.0, static_cast<double>(N_-i)));
}
return brevity_penalty(hyp_len, ref_len) * sum;
}
/*
* approx. bleu
*
* as in "Online Large-Margin Training of Syntactic
* and Structural Translation Features"
* (Chiang et al. '08)
*
* NOTE: needs some more code in dtrain.cc
*/
score_t
ApproxBleuScorer::Score(vector<WordID>& hyp, vector<WordID>& ref,
const unsigned rank, const unsigned src_len)
{
unsigned hyp_len = hyp.size(), ref_len = ref.size();
if (ref_len == 0) return 0.;
score_t score = 0.;
NgramCounts counts(N_);
if (hyp_len > 0) {
counts = make_ngram_counts(hyp, ref, N_);
NgramCounts tmp = glob_onebest_counts_ + counts;
score = Bleu(tmp, hyp_len, ref_len);
}
if (rank == 0) { // 'context of 1best translations'
glob_onebest_counts_ += counts;
glob_onebest_counts_ *= discount_;
glob_hyp_len_ = discount_ * (glob_hyp_len_ + hyp_len);
glob_ref_len_ = discount_ * (glob_ref_len_ + ref_len);
glob_src_len_ = discount_ * (glob_src_len_ + src_len);
}
return (score_t)glob_src_len_ * score;
}
} // namespace
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