From 1b8181bf0d6e9137e6b9ccdbe414aec37377a1a9 Mon Sep 17 00:00:00 2001 From: Chris Dyer Date: Sun, 18 Nov 2012 13:35:42 -0500 Subject: major restructure of the training code --- training/dtrain/score.cc | 254 +++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 254 insertions(+) create mode 100644 training/dtrain/score.cc (limited to 'training/dtrain/score.cc') diff --git a/training/dtrain/score.cc b/training/dtrain/score.cc new file mode 100644 index 00000000..34fc86a9 --- /dev/null +++ b/training/dtrain/score.cc @@ -0,0 +1,254 @@ +#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 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& hyp, vector& 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& hyp, vector& 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 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 (with N=4) + */ +score_t +SmoothBleuScorer::Score(vector& hyp, vector& 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 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, (double)(N_-i)); + } + return brevity_penalty(hyp_len, ref_len) * sum; +} + +/* + * 'sum' bleu + * + * sum up Ngram precisions + */ +score_t +SumBleuScorer::Score(vector& hyp, vector& 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(2.0, (double) (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& hyp, vector& 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(2.0, (double) (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& hyp, vector& 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 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(2.0, (double) (N_-j+1)); + j++; + } + 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 . + * No scaling by src len. + */ +score_t +ApproxBleuScorer::Score(vector& hyp, vector& 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; +} + +/* + * Linear (Corpus) Bleu + * + * as in "Lattice Minimum Bayes-Risk Decoding + * for Statistical Machine Translation" + * (Tromble et al. '08) + * + */ +score_t +LinearBleuScorer::Score(vector& hyp, vector& 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; +} + + +} // namespace + -- cgit v1.2.3 From 08d5de939f85075fc1569ddfa545b5d815231c3f Mon Sep 17 00:00:00 2001 From: Patrick Simianer Date: Fri, 15 Mar 2013 09:56:26 +0100 Subject: added fixed BLEU+1 --- training/dtrain/dtrain.cc | 2 ++ training/dtrain/score.cc | 31 ++++++++++++++++++++++++++++++- training/dtrain/score.h | 5 +++++ 3 files changed, 37 insertions(+), 1 deletion(-) (limited to 'training/dtrain/score.cc') diff --git a/training/dtrain/dtrain.cc b/training/dtrain/dtrain.cc index b317c365..53487d34 100644 --- a/training/dtrain/dtrain.cc +++ b/training/dtrain/dtrain.cc @@ -163,6 +163,8 @@ main(int argc, char** argv) scorer = dynamic_cast(new BleuScorer); } else if (scorer_str == "stupid_bleu") { scorer = dynamic_cast(new StupidBleuScorer); + } else if (scorer_str == "fixed_stupid_bleu") { + scorer = dynamic_cast(new FixedStupidBleuScorer); } else if (scorer_str == "smooth_bleu") { scorer = dynamic_cast(new SmoothBleuScorer); } else if (scorer_str == "sum_bleu") { diff --git a/training/dtrain/score.cc b/training/dtrain/score.cc index 34fc86a9..96d6e10a 100644 --- a/training/dtrain/score.cc +++ b/training/dtrain/score.cc @@ -49,7 +49,7 @@ BleuScorer::Score(vector& hyp, vector& ref, * for Machine Translation" * (Lin & Och '04) * - * NOTE: 0 iff no 1gram match + * NOTE: 0 iff no 1gram match ('grounded') */ score_t StupidBleuScorer::Score(vector& hyp, vector& ref, @@ -73,6 +73,35 @@ StupidBleuScorer::Score(vector& hyp, vector& ref, return brevity_penalty(hyp_len, ref_len) * exp(sum); } +/* + * fixed 'stupid' bleu + * + * as in "Optimizing for Sentence-Level BLEU+1 + * Yields Short Translations" + * (Nakov et al. '12) + */ +score_t +FixedStupidBleuScorer::Score(vector& hyp, vector& 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 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+1) * exp(sum); // <- fix +} + /* * smooth bleu * diff --git a/training/dtrain/score.h b/training/dtrain/score.h index f317c903..bddaa071 100644 --- a/training/dtrain/score.h +++ b/training/dtrain/score.h @@ -148,6 +148,11 @@ struct StupidBleuScorer : public LocalScorer score_t Score(vector& hyp, vector& ref, const unsigned /*rank*/, const unsigned /*src_len*/); }; +struct FixedStupidBleuScorer : public LocalScorer +{ + score_t Score(vector& hyp, vector& ref, const unsigned /*rank*/, const unsigned /*src_len*/); +}; + struct SmoothBleuScorer : public LocalScorer { score_t Score(vector& hyp, vector& ref, const unsigned /*rank*/, const unsigned /*src_len*/); -- cgit v1.2.3