#ifndef _DTRAIN_SCORE_H_ #define _DTRAIN_SCORE_H_ #include "dtrain.h" namespace dtrain { struct NgramCounts { size_t N_; map clipped_; map sum_; NgramCounts(const size_t N) : N_(N) { Zero(); } inline void operator+=(const NgramCounts& rhs) { if (rhs.N_ > N_) Resize(rhs.N_); for (size_t i = 0; i < N_; i++) { this->clipped_[i] += rhs.clipped_.find(i)->second; this->sum_[i] += rhs.sum_.find(i)->second; } } inline const NgramCounts operator+(const NgramCounts &other) const { NgramCounts result = *this; result += other; return result; } inline void Add(const size_t count, const size_t ref_count, const size_t i) { assert(i < N_); if (count > ref_count) { clipped_[i] += ref_count; } else { clipped_[i] += count; } sum_[i] += count; } inline void Zero() { for (size_t i = 0; i < N_; i++) { clipped_[i] = 0.; sum_[i] = 0.; } } inline void Resize(size_t N) { if (N == N_) return; else if (N > N_) { for (size_t i = N_; i < N; i++) { clipped_[i] = 0.; sum_[i] = 0.; } } else { // N < N_ for (size_t i = N_-1; i > N-1; i--) { clipped_.erase(i); sum_.erase(i); } } N_ = N; } }; typedef map, size_t> Ngrams; inline Ngrams MakeNgrams(const vector& s, const size_t N) { Ngrams ngrams; vector ng; for (size_t i = 0; i < s.size(); i++) { ng.clear(); for (size_t j = i; j < min(i+N, s.size()); j++) { ng.push_back(s[j]); ngrams[ng]++; } } return ngrams; } inline NgramCounts MakeNgramCounts(const vector& hyp, const vector& ref, const size_t N) { Ngrams hyp_ngrams = MakeNgrams(hyp, N); NgramCounts counts(N); Ngrams::iterator it, ti; for (it = hyp_ngrams.begin(); it != hyp_ngrams.end(); it++) { size_t max_ref_count = 0; for (auto r: ref) { ti = r.find(it->first); if (ti != r.end()) max_ref_count = max(max_ref_count, ti->second); } counts.Add(it->second, min(it->second, max_ref_count), it->first.size()-1); } return counts; } /* * per-sentence BLEU * 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] * */ struct PerSentenceBleuScorer { const size_t N_; vector w_; PerSentenceBleuScorer(size_t n) : N_(n) { for (size_t i = 1; i <= N_; i++) w_.push_back(1.0/N_); } 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& 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::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; } weight_t Score(const vector& hyp, const vector& ref_ngs, const vector& 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 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); } }; } // namespace #endif