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-rw-r--r--dtrain/score.cc254
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diff --git a/dtrain/score.cc b/dtrain/score.cc
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--- a/dtrain/score.cc
+++ /dev/null
@@ -1,254 +0,0 @@
-#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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