diff options
Diffstat (limited to 'training')
| -rw-r--r-- | training/model1.cc | 39 |
1 files changed, 36 insertions, 3 deletions
diff --git a/training/model1.cc b/training/model1.cc index b9590ece..346c0033 100644 --- a/training/model1.cc +++ b/training/model1.cc @@ -20,6 +20,10 @@ bool InitCommandLine(int argc, char** argv, po::variables_map* conf) { ("iterations,i",po::value<unsigned>()->default_value(5),"Number of iterations of EM training") ("beam_threshold,t",po::value<double>()->default_value(-4),"log_10 of beam threshold (-10000 to include everything, 0 max)") ("no_null_word,N","Do not generate from the null token") + ("write_alignments,A", "Write alignments instead of parameters") + ("favor_diagonal,d", "Use a static alignment distribution that assigns higher probabilities to alignments near the diagonal") + ("diagonal_tension,T", po::value<double>()->default_value(4.0), "How sharp or flat around the diagonal is the alignment distribution (<1 = flat >1 = sharp)") + ("prob_align_null", po::value<double>()->default_value(0.08), "When --favor_diagonal is set, what's the probability of a null alignment?") ("variational_bayes,v","Add a symmetric Dirichlet prior and infer VB estimate of weights") ("alpha,a", po::value<double>()->default_value(0.01), "Hyperparameter for optional Dirichlet prior") ("no_add_viterbi,V","Do not add Viterbi alignment points (may generate a grammar where some training sentence pairs are unreachable)"); @@ -56,7 +60,12 @@ int main(int argc, char** argv) { const WordID kNULL = TD::Convert("<eps>"); const bool add_viterbi = (conf.count("no_add_viterbi") == 0); const bool variational_bayes = (conf.count("variational_bayes") > 0); + const bool write_alignments = (conf.count("write_alignments") > 0); + const double diagonal_tension = conf["diagonal_tension"].as<double>(); + const double prob_align_null = conf["prob_align_null"].as<double>(); + const double prob_align_not_null = 1.0 - prob_align_null; const double alpha = conf["alpha"].as<double>(); + const bool favor_diagonal = conf.count("favor_diagonal"); if (variational_bayes && alpha <= 0.0) { cerr << "--alpha must be > 0\n"; return 1; @@ -93,31 +102,52 @@ int main(int argc, char** argv) { denom += trg.size(); vector<double> probs(src.size() + 1); const double src_logprob = -log(src.size() + 1); + bool first_al = true; // used for write_alignments for (int j = 0; j < trg.size(); ++j) { const WordID& f_j = trg[j][0].label; double sum = 0; + const double j_over_ts = double(j) / trg.size(); + double prob_a_i = 1.0 / (src.size() + use_null); // uniform (model 1) if (use_null) { - probs[0] = tt.prob(kNULL, f_j); + if (favor_diagonal) prob_a_i = prob_align_null; + probs[0] = tt.prob(kNULL, f_j) * prob_a_i; sum += probs[0]; } + double az = 0; + if (favor_diagonal) { + for (int ta = 0; ta < src.size(); ++ta) + az += exp(-fabs(double(ta) / src.size() - j_over_ts) * diagonal_tension); + az /= prob_align_not_null; + } for (int i = 1; i <= src.size(); ++i) { - probs[i] = tt.prob(src[i-1][0].label, f_j); + if (favor_diagonal) + prob_a_i = exp(-fabs(double(i) / src.size() - j_over_ts) * diagonal_tension) / az; + probs[i] = tt.prob(src[i-1][0].label, f_j) * prob_a_i; sum += probs[i]; } if (final_iteration) { - if (add_viterbi) { + if (add_viterbi || write_alignments) { WordID max_i = 0; double max_p = -1; + int max_index = -1; if (use_null) { max_i = kNULL; + max_index = 0; max_p = probs[0]; } for (int i = 1; i <= src.size(); ++i) { if (probs[i] > max_p) { + max_index = i; max_p = probs[i]; max_i = src[i-1][0].label; } } + if (write_alignments) { + if (max_index > 0) { + if (first_al) first_al = false; else cout << ' '; + cout << (max_index - 1) << "-" << j; + } + } was_viterbi[max_i][f_j] = 1.0; } } else { @@ -128,6 +158,7 @@ int main(int argc, char** argv) { } likelihood += log(sum) + src_logprob; } + if (write_alignments && final_iteration) cout << endl; } // log(e) = 1.0 @@ -145,6 +176,8 @@ int main(int argc, char** argv) { tt.Normalize(); } } + if (write_alignments) return 0; + for (TTable::Word2Word2Double::iterator ei = tt.ttable.begin(); ei != tt.ttable.end(); ++ei) { const TTable::Word2Double& cpd = ei->second; const TTable::Word2Double& vit = was_viterbi[ei->first]; |