'pseudo model 2' that strictly favors a diagonal, with tunable parameters for p(null) and how sharp/flat the alignment distribution is around the diagonal

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];