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-rw-r--r--training/dtrain/score.h356
1 files changed, 288 insertions, 68 deletions
diff --git a/training/dtrain/score.h b/training/dtrain/score.h
index 06dbc5a4..ca3da39b 100644
--- a/training/dtrain/score.h
+++ b/training/dtrain/score.h
@@ -12,6 +12,8 @@ struct NgramCounts
map<size_t, weight_t> clipped_;
map<size_t, weight_t> sum_;
+ NgramCounts() {}
+
NgramCounts(const size_t N) : N_(N) { Zero(); }
inline void
@@ -24,13 +26,13 @@ struct NgramCounts
}
}
- inline const NgramCounts
- operator+(const NgramCounts &other) const
+ inline void
+ operator*=(const weight_t rhs)
{
- NgramCounts result = *this;
- result += other;
-
- return result;
+ for (unsigned i = 0; i < N_; i++) {
+ this->clipped_[i] *= rhs;
+ this->sum_[i] *= rhs;
+ }
}
inline void
@@ -112,85 +114,303 @@ MakeNgramCounts(const vector<WordID>& hyp,
return counts;
}
+class Scorer
+{
+ protected:
+ const size_t N_;
+ vector<weight_t> w_;
+
+ public:
+ Scorer(size_t n): N_(n)
+ {
+ for (size_t i = 1; i <= N_; i++)
+ w_.push_back(1.0/N_);
+ }
+
+ inline bool
+ Init(const vector<WordID>& hyp,
+ const vector<Ngrams>& ref_ngs,
+ const vector<size_t>& ref_ls,
+ size_t& hl,
+ size_t& rl,
+ size_t& M,
+ vector<weight_t>& v,
+ NgramCounts& counts)
+ {
+ hl = hyp.size();
+ if (hl == 0) return false;
+ rl = BestMatchLength(hl, ref_ls);
+ if (rl == 0) return false;
+ counts = MakeNgramCounts(hyp, ref_ngs, N_);
+ if (rl < N_) {
+ M = rl;
+ for (size_t i = 0; i < M; i++) v.push_back(1/((weight_t)M));
+ } else {
+ M = N_;
+ v = w_;
+ }
+
+ return true;
+ }
+
+ inline weight_t
+ BrevityPenalty(const size_t hl, const size_t rl)
+ {
+ if (hl > rl)
+ return 1;
+
+ return exp(1 - (weight_t)rl/hl);
+ }
+
+ inline size_t
+ BestMatchLength(const size_t hl,
+ const vector<size_t>& ref_ls)
+ {
+ size_t m;
+ if (ref_ls.size() == 1) {
+ m = ref_ls.front();
+ } else {
+ size_t i = 0, best_idx = 0;
+ size_t best = numeric_limits<size_t>::max();
+ for (auto l: ref_ls) {
+ size_t d = abs(hl-l);
+ if (d < best) {
+ best_idx = i;
+ best = d;
+ }
+ i += 1;
+ }
+ m = ref_ls[best_idx];
+ }
+
+ return m;
+ }
+
+ virtual weight_t
+ Score(const vector<WordID>&,
+ const vector<Ngrams>&,
+ const vector<size_t>&) = 0;
+
+ void
+ UpdateContext(const vector<WordID>& /*hyp*/,
+ const vector<Ngrams>& /*ref_ngs*/,
+ const vector<size_t>& /*ref_ls*/,
+ weight_t /*decay*/) {}
+};
+
/*
- * per-sentence BLEU
+ * 'fixed' per-sentence BLEU
+ * simply add 1 to reference length for calculation of BP
+ *
* as in "Optimizing for Sentence-Level BLEU+1
* Yields Short Translations"
* (Nakov et al. '12)
*
- * [simply add 1 to reference length for calculation of BP]
+ */
+class PerSentenceBleuScorer : public Scorer
+{
+ public:
+ PerSentenceBleuScorer(size_t n) : Scorer(n) {}
+
+ weight_t
+ Score(const vector<WordID>& hyp,
+ const vector<Ngrams>& ref_ngs,
+ const vector<size_t>& ref_ls)
+ {
+ size_t hl, rl, M;
+ vector<weight_t> v;
+ NgramCounts counts;
+ if (!Init(hyp, ref_ngs, ref_ls, hl, rl, M, v, counts))
+ return 0.;
+ weight_t sum=0, add=0;
+ for (size_t i = 0; i < M; i++) {
+ if (i == 0 && (counts.sum_[i] == 0 || counts.clipped_[i] == 0)) return 0.;
+ if (i > 0) add = 1;
+ sum += v[i] * log(((weight_t)counts.clipped_[i] + add)
+ / ((counts.sum_[i] + add)));
+ }
+
+ return BrevityPenalty(hl, rl+1) * exp(sum);
+ }
+};
+
+
+/*
+ * BLEU
+ * 0 if for one n \in {1..N} count is 0
+ *
+ * as in "BLEU: a Method for Automatic Evaluation
+ * of Machine Translation"
+ * (Papineni et al. '02)
*
*/
-struct PerSentenceBleuScorer
+
+class BleuScorer : public Scorer
{
- const size_t N_;
- vector<weight_t> w_;
+ public:
+ BleuScorer(size_t n) : Scorer(n) {}
- PerSentenceBleuScorer(size_t n) : N_(n)
- {
- for (size_t i = 1; i <= N_; i++)
- w_.push_back(1.0/N_);
- }
+ weight_t
+ Score(const vector<WordID>& hyp,
+ const vector<Ngrams>& ref_ngs,
+ const vector<size_t>& ref_ls)
+ {
+ size_t hl, rl, M;
+ vector<weight_t> v;
+ NgramCounts counts;
+ if (!Init(hyp, ref_ngs, ref_ls, hl, rl, M, v, counts))
+ return 0.;
+ weight_t sum = 0;
+ for (size_t i = 0; i < M; i++) {
+ if (counts.sum_[i] == 0 || counts.clipped_[i] == 0) return 0.;
+ sum += v[i] * log((weight_t)counts.clipped_[i]/counts.sum_[i]);
+ }
- inline weight_t
- BrevityPenalty(const size_t hl, const size_t rl)
- {
- if (hl > rl)
- return 1;
+ return BrevityPenalty(hl, rl) * exp(sum);
+ }
+};
- return exp(1 - (weight_t)rl/hl);
- }
+/*
+ * original BLEU+1
+ * 0 iff no 1gram match ('grounded')
+ *
+ * as in "ORANGE: a Method for Evaluating
+ * Automatic Evaluation Metrics
+ * for Machine Translation"
+ * (Lin & Och '04)
+ *
+ */
+class OriginalPerSentenceBleuScorer : public Scorer
+{
+ public:
+ OriginalPerSentenceBleuScorer(size_t n) : Scorer(n) {}
- inline size_t
- BestMatchLength(const size_t hl,
- const vector<size_t>& ref_ls)
- {
- size_t m;
- if (ref_ls.size() == 1) {
- m = ref_ls.front();
- } else {
- size_t i = 0, best_idx = 0;
- size_t best = numeric_limits<size_t>::max();
- for (auto l: ref_ls) {
- size_t d = abs(hl-l);
- if (d < best) {
- best_idx = i;
- best = d;
+ weight_t
+ Score(const vector<WordID>& hyp,
+ const vector<Ngrams>& ref_ngs,
+ const vector<size_t>& ref_ls)
+ {
+ size_t hl, rl, M;
+ vector<weight_t> v;
+ NgramCounts counts;
+ if (!Init(hyp, ref_ngs, ref_ls, hl, rl, M, v, counts))
+ return 0.;
+ weight_t sum=0, add=0;
+ for (size_t 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(((weight_t)counts.clipped_[i] + add)/((counts.sum_[i] + add)));
+ }
+
+ return BrevityPenalty(hl, rl) * exp(sum);
+ }
+};
+
+/*
+ * smooth BLEU
+ * max is 0.9375 (with N=4)
+ *
+ * as in "An End-to-End Discriminative Approach
+ * to Machine Translation"
+ * (Liang et al. '06)
+ *
+ */
+class SmoothPerSentenceBleuScorer : public Scorer
+{
+ public:
+ SmoothPerSentenceBleuScorer(size_t n) : Scorer(n) {}
+
+ weight_t
+ Score(const vector<WordID>& hyp,
+ const vector<Ngrams>& ref_ngs,
+ const vector<size_t>& ref_ls)
+ {
+ size_t hl=hyp.size(), rl=BestMatchLength(hl, ref_ls);
+ if (hl == 0 || rl == 0) return 0.;
+ NgramCounts counts = MakeNgramCounts(hyp, ref_ngs, N_);
+ size_t M = N_;
+ if (rl < N_) M = rl;
+ weight_t sum = 0.;
+ vector<weight_t> i_bleu;
+ for (size_t i=0; i < M; i++)
+ i_bleu.push_back(0.);
+ for (size_t i=0; i < M; i++) {
+ if (counts.sum_[i] == 0 || counts.clipped_[i] == 0) {
+ break;
+ } else {
+ weight_t i_score = log((weight_t)counts.clipped_[i]/counts.sum_[i]);
+ for (size_t j=i; j < M; j++) {
+ i_bleu[j] += (1/((weight_t)j+1)) * i_score;
+ }
}
- i += 1;
+ sum += exp(i_bleu[i])/pow(2.0, (double)(N_-i+2));
}
- m = ref_ls[best_idx];
+
+ return BrevityPenalty(hl, hl) * sum;
+ }
+};
+
+/*
+ * approx. bleu
+ * Needs some more code in dtrain.cc .
+ * We do not scaling by source lengths, as hypotheses are compared only
+ * within an kbest list, not globally.
+ *
+ * as in "Online Large-Margin Training of Syntactic
+ * and Structural Translation Features"
+ * (Chiang et al. '08)
+ *
+
+ */
+class ApproxBleuScorer : public Scorer
+{
+ private:
+ NgramCounts context;
+ weight_t hyp_sz_sum;
+ weight_t ref_sz_sum;
+
+ public:
+ ApproxBleuScorer(size_t n) :
+ Scorer(n), context(n), hyp_sz_sum(0), ref_sz_sum(0) {}
+
+ weight_t
+ Score(const vector<WordID>& hyp,
+ const vector<Ngrams>& ref_ngs,
+ const vector<size_t>& ref_ls)
+ {
+ size_t hl, rl, M;
+ vector<weight_t> v;
+ NgramCounts counts;
+ if (!Init(hyp, ref_ngs, ref_ls, hl, rl, M, v, counts))
+ return 0.;
+ counts += context;
+ weight_t sum = 0;
+ for (size_t i = 0; i < M; i++) {
+ if (counts.sum_[i] == 0 || counts.clipped_[i] == 0) return 0.;
+ sum += v[i] * log((weight_t)counts.clipped_[i]/counts.sum_[i]);
+ }
+
+ return BrevityPenalty(hyp_sz_sum+hl, ref_sz_sum+rl) * exp(sum);
}
- return m;
- }
+ void
+ UpdateContext(const vector<WordID>& hyp,
+ const vector<Ngrams>& ref_ngs,
+ const vector<size_t>& ref_ls,
+ weight_t decay=0.9)
+ {
+ size_t hl, rl, M;
+ vector<weight_t> v;
+ NgramCounts counts;
+ Init(hyp, ref_ngs, ref_ls, hl, rl, M, v, counts);
- weight_t
- Score(const vector<WordID>& hyp,
- const vector<Ngrams>& ref_ngs,
- const vector<size_t>& ref_ls)
- {
- size_t hl = hyp.size(), rl = 0;
- if (hl == 0) return 0.;
- rl = BestMatchLength(hl, ref_ls);
- if (rl == 0) return 0.;
- NgramCounts counts = MakeNgramCounts(hyp, ref_ngs, N_);
- size_t M = N_;
- vector<weight_t> v = w_;
- if (rl < N_) {
- M = rl;
- for (size_t i = 0; i < M; i++) v[i] = 1/((weight_t)M);
- }
- weight_t sum = 0, add = 0;
- for (size_t i = 0; i < M; i++) {
- if (i == 0 && (counts.sum_[i] == 0 || counts.clipped_[i] == 0)) return 0.;
- if (i > 0) add = 1;
- sum += v[i] * log(((weight_t)counts.clipped_[i] + add)
- / ((counts.sum_[i] + add)));
- }
-
- return BrevityPenalty(hl, rl+1) * exp(sum);
- }
+ context += counts;
+ context *= decay;
+ hyp_sz_sum += hl;
+ hyp_sz_sum *= decay;
+ ref_sz_sum += rl;
+ ref_sz_sum *= decay;
+ }
};
} // namespace
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