diff options
Diffstat (limited to 'dtrain/score.cc')
| -rw-r--r-- | dtrain/score.cc | 117 |
1 files changed, 113 insertions, 4 deletions
diff --git a/dtrain/score.cc b/dtrain/score.cc index 7b1f6be4..4a7cac6e 100644 --- a/dtrain/score.cc +++ b/dtrain/score.cc @@ -80,7 +80,7 @@ StupidBleuScorer::Score(vector<WordID>& hyp, vector<WordID>& ref, * to Machine Translation" * (Liang et al. '06) * - * NOTE: max is 0.9375 + * NOTE: max is 0.9375 (with N=4) */ score_t SmoothBleuScorer::Score(vector<WordID>& hyp, vector<WordID>& ref, @@ -103,7 +103,83 @@ SmoothBleuScorer::Score(vector<WordID>& hyp, vector<WordID>& ref, i_bleu[j] += (1/((score_t)j+1)) * i_ng; } } - sum += exp(i_bleu[i])/(pow(2.0, static_cast<double>(N_-i))); + sum += exp(i_bleu[i])/(pow(2.0, N_-i)); + } + return brevity_penalty(hyp_len, ref_len) * sum; +} + +/* + * 'sum' bleu + * + * sum up Ngram precisions + */ +score_t +SumBleuScorer::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.; + unsigned j = 1; + for (unsigned i = 0; i < M; i++) { + if (counts.sum_[i] == 0 || counts.clipped_[i] == 0) break; + sum += ((score_t)counts.clipped_[i]/counts.sum_[i])/pow(2., N_-j+1); + j++; + } + return brevity_penalty(hyp_len, ref_len) * sum; +} + +/* + * 'sum' (exp) bleu + * + * sum up exp(Ngram precisions) + */ +score_t +SumExpBleuScorer::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.; + unsigned j = 1; + for (unsigned i = 0; i < M; i++) { + if (counts.sum_[i] == 0 || counts.clipped_[i] == 0) break; + sum += exp(((score_t)counts.clipped_[i]/counts.sum_[i]))/pow(2., N_-j+1); + j++; + } + return brevity_penalty(hyp_len, ref_len) * sum; +} + +/* + * 'sum' (whatever) bleu + * + * sum up exp(weight * log(Ngram precisions)) + */ +score_t +SumWhateverBleuScorer::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.; + unsigned j = 1; + for (unsigned i = 0; i < M; i++) { + if (counts.sum_[i] == 0 || counts.clipped_[i] == 0) break; + sum += exp(v[i] * log(((score_t)counts.clipped_[i]/counts.sum_[i])))/pow(2., N_-j+1); + j++; } return brevity_penalty(hyp_len, ref_len) * sum; } @@ -115,7 +191,8 @@ SmoothBleuScorer::Score(vector<WordID>& hyp, vector<WordID>& ref, * and Structural Translation Features" * (Chiang et al. '08) * - * NOTE: needs some more code in dtrain.cc + * NOTE: Needs some more code in dtrain.cc . + * No scaling by src len. */ score_t ApproxBleuScorer::Score(vector<WordID>& hyp, vector<WordID>& ref, @@ -137,7 +214,39 @@ ApproxBleuScorer::Score(vector<WordID>& hyp, vector<WordID>& ref, 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; + return score; +} + +/* + * Linear (Corpus) Bleu + * + * as in "Lattice Minimum Bayes-Risk Decoding + * for Statistical Machine Translation" + * (Tromble et al. '08) + * + */ +score_t +LinearBleuScorer::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.; + unsigned M = N_; + if (ref_len < N_) M = ref_len; + NgramCounts counts(M); + if (hyp_len > 0) + counts = make_ngram_counts(hyp, ref, M); + score_t ret = 0.; + for (unsigned i = 0; i < M; i++) { + if (counts.sum_[i] == 0 || onebest_counts_.sum_[i] == 0) break; + ret += counts.sum_[i]/onebest_counts_.sum_[i]; + } + ret = -(hyp_len/(score_t)onebest_len_) + (1./M) * ret; + if (rank == 0) { + onebest_len_ += hyp_len; + onebest_counts_ += counts; + } + return ret; } |