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-rw-r--r--training/dtrain/score.cc254
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diff --git a/training/dtrain/score.cc b/training/dtrain/score.cc
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+++ b/training/dtrain/score.cc
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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 (with N=4)
+ */
+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, (double)(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(2.0, (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<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(2.0, (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<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(2.0, (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<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;
+}
+
+/*
+ * 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;
+}
+
+
+} // namespace
+
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