9 files changed, 561 insertions, 38 deletions

diff --git a/word-aligner/Makefile.am b/word-aligner/Makefile.am
new file mode 100644
index 00000000..2dcb688e
--- /dev/null
+++ b/word-aligner/Makefile.am
@@ -0,0 +1,6 @@
+bin_PROGRAMS = fast_align
+
+fast_align_SOURCES = fast_align.cc ttables.cc
+fast_align_LDADD = $(top_srcdir)/utils/libutils.a
+
+AM_CPPFLAGS = -W -Wall $(GTEST_CPPFLAGS) -I$(top_srcdir)/utils -I$(top_srcdir)/training
diff --git a/word-aligner/aligner.pl b/word-aligner/aligner.pl
index c5078645..cbccb94a 100755
--- a/word-aligner/aligner.pl
+++ b/word-aligner/aligner.pl
@@ -51,6 +51,8 @@ while(<IN>) {
   chomp;
   my ($f, $e) = split / \|\|\| /;
   die "Bad format, excepted ||| separated line" unless defined $f && defined $e;
+  $f =~ s/\[/(/g;
+  $e =~ s/\]/)/g;
   print F "$f\n";
   print E "$e\n";
 }
@@ -80,6 +82,11 @@ NCLASSES = $num_classes
 TARGETS = @targets
 PTRAIN = \$(TRAINING_DIR)/cluster-ptrain.pl --restart_if_necessary
 PTRAIN_PARAMS = --gaussian_prior --sigma_squared 1.0 --max_iteration 15
+#MPIJOBS = 4
+#MPIRUN = mpirun -np $(MPIJOBS)
+MPIRUN=
+
+WALLTIME=90
 
 export
 
@@ -99,7 +106,15 @@ clean:
 EOT
 close TOPLEVEL;
 
-print STDERR "Created alignment task. chdir to talign/ then type make.\n\n";
+print STDERR <<EOT;
+Created alignment task. To start, run:
+cd talign/
+make
+
+To specify the walltime *in minutes* used by the optimizer, use
+make WALLTIME=120
+
+EOT
 exit 0;
 
 sub make_stage {
@@ -142,6 +157,8 @@ EOT
   open AGENDA, ">$stage_dir/agenda.txt" or die "Can't write $stage_dir/agenda.txt: $!";
   print AGENDA "cdec.ini $TRAINING_ITERATIONS\n";
   close AGENDA;
+  `cp $SCRIPT_DIR/makefiles/makefile.model.$direction $stage_dir/Makefile`;
+  die unless $? == 0;
 }
 
 sub usage {
diff --git a/word-aligner/da.h b/word-aligner/da.h
new file mode 100644
index 00000000..c979b641
--- /dev/null
+++ b/word-aligner/da.h
@@ -0,0 +1,79 @@
+#ifndef _DA_H_
+#define _DA_H_
+
+#include <cmath>
+#include <cassert>
+
+// m = trg len
+// n = src len
+// i = trg index
+// j = src index
+struct DiagonalAlignment {
+
+  static double UnnormalizedProb(const unsigned i, const unsigned j, const unsigned m, const unsigned n, const double alpha) {
+#if 0
+    assert(i > 0);
+    assert(n > 0);
+    assert(m >= i);
+    assert(n >= j);
+#endif
+    return exp(Feature(i, j, m, n) * alpha);
+  }
+
+  static double ComputeZ(const unsigned i, const unsigned m, const unsigned n, const double alpha) {
+#if 0
+    assert(i > 0);
+    assert(n > 0);
+    assert(m >= i);
+#endif
+    const double split = double(i) * n / m;
+    const unsigned floor = split;
+    unsigned ceil = floor + 1;
+    const double ratio = exp(-alpha / n);
+    const unsigned num_top = n - floor;
+    double ezt = 0;
+    double ezb = 0;
+    if (num_top)
+      ezt = UnnormalizedProb(i, ceil, m, n, alpha) * (1.0 - pow(ratio, num_top)) / (1.0 - ratio);
+    if (floor)
+      ezb = UnnormalizedProb(i, floor, m, n, alpha) * (1.0 - pow(ratio, floor)) / (1.0 - ratio);
+    return ezb + ezt;
+  }
+
+  static double ComputeDLogZ(const unsigned i, const unsigned m, const unsigned n, const double alpha) {
+    const double z = ComputeZ(i, n, m, alpha);
+    const double split = double(i) * n / m;
+    const unsigned floor = split;
+    const unsigned ceil = floor + 1;
+    const double ratio = exp(-alpha / n);
+    const double d = -1.0 / n;
+    const unsigned num_top = n - floor;
+    double pct = 0;
+    double pcb = 0;
+    if (num_top) {
+      pct = arithmetico_geometric_series(Feature(i, ceil, m, n), UnnormalizedProb(i, ceil, m, n, alpha), ratio, d, num_top);
+      //cerr << "PCT = " << pct << endl;
+    }
+    if (floor) {
+      pcb = arithmetico_geometric_series(Feature(i, floor, m, n), UnnormalizedProb(i, floor, m, n, alpha), ratio, d, floor);
+      //cerr << "PCB = " << pcb << endl;
+    }
+    return (pct + pcb) / z;
+  }
+
+  inline static double Feature(const unsigned i, const unsigned j, const unsigned m, const unsigned n) {
+    return -fabs(double(j) / n - double(i) / m);
+  }
+
+ private:
+  inline static double arithmetico_geometric_series(const double a_1, const double g_1, const double r, const double d, const unsigned n) {
+    const double g_np1 = g_1 * pow(r, n);
+    const double a_n = d * (n - 1) + a_1;
+    const double x_1 = a_1 * g_1;
+    const double g_2 = g_1 * r;
+    const double rm1 = r - 1;
+    return (a_n * g_np1 - x_1) / rm1 - d*(g_np1 - g_2) / (rm1 * rm1);
+  }
+};
+
+#endif
diff --git a/word-aligner/fast_align.cc b/word-aligner/fast_align.cc
new file mode 100644
index 00000000..9d698074
--- /dev/null
+++ b/word-aligner/fast_align.cc
@@ -0,0 +1,310 @@
+#include <iostream>
+#include <cmath>
+#include <utility>
+#include <tr1/unordered_map>
+
+#include <boost/functional/hash.hpp>
+#include <boost/program_options.hpp>
+#include <boost/program_options/variables_map.hpp>
+
+#include "m.h"
+#include "corpus_tools.h"
+#include "stringlib.h"
+#include "filelib.h"
+#include "ttables.h"
+#include "tdict.h"
+#include "da.h"
+
+namespace po = boost::program_options;
+using namespace std;
+using namespace std::tr1;
+
+bool InitCommandLine(int argc, char** argv, po::variables_map* conf) {
+  po::options_description opts("Configuration options");
+  opts.add_options()
+        ("input,i",po::value<string>(),"Parallel corpus input file")
+        ("reverse,r","Reverse estimation (swap source and target during training)")
+        ("iterations,I",po::value<unsigned>()->default_value(5),"Number of iterations of EM training")
+        //("bidir,b", "Run bidirectional alignment")
+        ("favor_diagonal,d", "Use a static alignment distribution that assigns higher probabilities to alignments near the diagonal")
+        ("prob_align_null", po::value<double>()->default_value(0.08), "When --favor_diagonal is set, what's the probability of a null alignment?")
+        ("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)")
+        ("optimize_tension,o", "Optimize diagonal tension during EM")
+        ("variational_bayes,v","Infer VB estimate of parameters under a symmetric Dirichlet prior")
+        ("alpha,a", po::value<double>()->default_value(0.01), "Hyperparameter for optional Dirichlet prior")
+        ("no_null_word,N","Do not generate from a null token")
+        ("output_parameters,p", "Write model parameters instead of alignments")
+        ("beam_threshold,t",po::value<double>()->default_value(-4),"When writing parameters, log_10 of beam threshold for writing parameter (-10000 to include everything, 0 max parameter only)")
+        ("hide_training_alignments,H", "Hide training alignments (only useful if you want to use -x option and just compute testset statistics)")
+        ("testset,x", po::value<string>(), "After training completes, compute the log likelihood of this set of sentence pairs under the learned model")
+        ("no_add_viterbi,V","When writing model parameters, do not add Viterbi alignment points (may generate a grammar where some training sentence pairs are unreachable)");
+  po::options_description clo("Command line options");
+  clo.add_options()
+        ("config", po::value<string>(), "Configuration file")
+        ("help,h", "Print this help message and exit");
+  po::options_description dconfig_options, dcmdline_options;
+  dconfig_options.add(opts);
+  dcmdline_options.add(opts).add(clo);
+  
+  po::store(parse_command_line(argc, argv, dcmdline_options), *conf);
+  if (conf->count("config")) {
+    ifstream config((*conf)["config"].as<string>().c_str());
+    po::store(po::parse_config_file(config, dconfig_options), *conf);
+  }
+  po::notify(*conf);
+
+  if (conf->count("help") || conf->count("input") == 0) {
+    cerr << "Usage " << argv[0] << " [OPTIONS] -i corpus.fr-en\n";
+    cerr << dcmdline_options << endl;
+    return false;
+  }
+  return true;
+}
+
+int main(int argc, char** argv) {
+  po::variables_map conf;
+  if (!InitCommandLine(argc, argv, &conf)) return 1;
+  const string fname = conf["input"].as<string>();
+  const bool reverse = conf.count("reverse") > 0;
+  const int ITERATIONS = conf["iterations"].as<unsigned>();
+  const double BEAM_THRESHOLD = pow(10.0, conf["beam_threshold"].as<double>());
+  const bool use_null = (conf.count("no_null_word") == 0);
+  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("output_parameters") == 0);
+  double diagonal_tension = conf["diagonal_tension"].as<double>();
+  bool optimize_tension = conf.count("optimize_tension");
+  const bool hide_training_alignments = (conf.count("hide_training_alignments") > 0);
+  string testset;
+  if (conf.count("testset")) testset = conf["testset"].as<string>();
+  double prob_align_null = conf["prob_align_null"].as<double>();
+  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;
+  }
+
+  TTable s2t, t2s;
+  TTable::Word2Word2Double s2t_viterbi;
+  unordered_map<pair<short, short>, unsigned, boost::hash<pair<short, short> > > size_counts;
+  double tot_len_ratio = 0;
+  double mean_srclen_multiplier = 0;
+  vector<double> probs;
+  for (int iter = 0; iter < ITERATIONS; ++iter) {
+    const bool final_iteration = (iter == (ITERATIONS - 1));
+    cerr << "ITERATION " << (iter + 1) << (final_iteration ? " (FINAL)" : "") << endl;
+    ReadFile rf(fname);
+    istream& in = *rf.stream();
+    double likelihood = 0;
+    double denom = 0.0;
+    int lc = 0;
+    bool flag = false;
+    string line;
+    string ssrc, strg;
+    vector<WordID> src, trg;
+    double c0 = 0;
+    double emp_feat = 0;
+    double toks = 0;
+    while(true) {
+      getline(in, line);
+      if (!in) break;
+      ++lc;
+      if (lc % 1000 == 0) { cerr << '.'; flag = true; }
+      if (lc %50000 == 0) { cerr << " [" << lc << "]\n" << flush; flag = false; }
+      src.clear(); trg.clear();
+      CorpusTools::ReadLine(line, &src, &trg);
+      if (reverse) swap(src, trg);
+      if (src.size() == 0 || trg.size() == 0) {
+        cerr << "Error: " << lc << "\n" << line << endl;
+        return 1;
+      }
+      if (iter == 0)
+        tot_len_ratio += static_cast<double>(trg.size()) / static_cast<double>(src.size());
+      denom += trg.size();
+      probs.resize(src.size() + 1);
+      if (iter == 0)
+        ++size_counts[make_pair<short,short>(trg.size(), src.size())];
+      bool first_al = true;  // used for write_alignments
+      toks += trg.size();
+      for (unsigned j = 0; j < trg.size(); ++j) {
+        const WordID& f_j = trg[j];
+        double sum = 0;
+        double prob_a_i = 1.0 / (src.size() + use_null);  // uniform (model 1)
+        if (use_null) {
+          if (favor_diagonal) prob_a_i = prob_align_null;
+          probs[0] = s2t.prob(kNULL, f_j) * prob_a_i;
+          sum += probs[0];
+        }
+        double az = 0;
+        if (favor_diagonal)
+          az = DiagonalAlignment::ComputeZ(j+1, trg.size(), src.size(), diagonal_tension) / prob_align_not_null;
+        for (unsigned i = 1; i <= src.size(); ++i) {
+          if (favor_diagonal)
+            prob_a_i = DiagonalAlignment::UnnormalizedProb(j + 1, i, trg.size(), src.size(), diagonal_tension) / az;
+          probs[i] = s2t.prob(src[i-1], f_j) * prob_a_i;
+          sum += probs[i];
+        }
+        if (final_iteration) {
+          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 (unsigned i = 1; i <= src.size(); ++i) {
+              if (probs[i] > max_p) {
+                max_index = i;
+                max_p = probs[i];
+                max_i = src[i-1];
+              }
+            }
+            if (!hide_training_alignments && write_alignments) {
+              if (max_index > 0) {
+                if (first_al) first_al = false; else cout << ' ';
+                if (reverse)
+                  cout << j << '-' << (max_index - 1);
+                else
+                  cout << (max_index - 1) << '-' << j;
+              }
+            }
+            s2t_viterbi[max_i][f_j] = 1.0;
+          }
+        } else {
+          if (use_null) {
+            double count = probs[0] / sum;
+            c0 += count;
+            s2t.Increment(kNULL, f_j, count);
+          }
+          for (unsigned i = 1; i <= src.size(); ++i) {
+            const double p = probs[i] / sum;
+            s2t.Increment(src[i-1], f_j, p);
+            emp_feat += DiagonalAlignment::Feature(j, i, trg.size(), src.size()) * p;
+          }
+        }
+        likelihood += log(sum);
+      }
+      if (write_alignments && final_iteration && !hide_training_alignments) cout << endl;
+    }
+
+    // log(e) = 1.0
+    double base2_likelihood = likelihood / log(2);
+
+    if (flag) { cerr << endl; }
+    if (iter == 0) {
+      mean_srclen_multiplier = tot_len_ratio / lc;
+      cerr << "expected target length = source length * " << mean_srclen_multiplier << endl;
+    }
+    emp_feat /= toks;
+    cerr << "  log_e likelihood: " << likelihood << endl;
+    cerr << "  log_2 likelihood: " << base2_likelihood << endl;
+    cerr << "     cross entropy: " << (-base2_likelihood / denom) << endl;
+    cerr << "        perplexity: " << pow(2.0, -base2_likelihood / denom) << endl;
+    cerr << "      posterior p0: " << c0 / toks << endl;
+    cerr << " posterior al-feat: " << emp_feat << endl;
+    //cerr << "     model tension: " << mod_feat / toks << endl;
+    cerr << "       size counts: " << size_counts.size() << endl;
+    if (!final_iteration) {
+      if (favor_diagonal && optimize_tension && iter > 0) {
+        for (int ii = 0; ii < 8; ++ii) {
+          double mod_feat = 0;
+          unordered_map<pair<short,short>,unsigned>::iterator it = size_counts.begin();
+          for(; it != size_counts.end(); ++it) {
+            const pair<short,short>& p = it->first;
+            for (short j = 1; j <= p.first; ++j)
+              mod_feat += it->second * DiagonalAlignment::ComputeDLogZ(j, p.first, p.second, diagonal_tension);
+          }
+          mod_feat /= toks;
+          cerr << "  " << ii + 1 << "  model al-feat: " << mod_feat << " (tension=" << diagonal_tension << ")\n";
+          diagonal_tension += (emp_feat - mod_feat) * 20.0;
+          if (diagonal_tension <= 0.1) diagonal_tension = 0.1;
+          if (diagonal_tension > 14) diagonal_tension = 14;
+        }
+        cerr << "     final tension: " << diagonal_tension << endl;
+      }
+      if (variational_bayes)
+        s2t.NormalizeVB(alpha);
+      else
+        s2t.Normalize();
+      //prob_align_null *= 0.8; // XXX
+      //prob_align_null += (c0 / toks) * 0.2;
+      prob_align_not_null = 1.0 - prob_align_null;
+    }
+  }
+  if (testset.size()) {
+    ReadFile rf(testset);
+    istream& in = *rf.stream();
+    int lc = 0;
+    double tlp = 0;
+    string line;
+    while (getline(in, line)) {
+      ++lc;
+      vector<WordID> src, trg;
+      CorpusTools::ReadLine(line, &src, &trg);
+      cout << TD::GetString(src) << " ||| " << TD::GetString(trg) << " |||";
+      if (reverse) swap(src, trg);
+      double log_prob = Md::log_poisson(trg.size(), 0.05 + src.size() * mean_srclen_multiplier);
+
+      // compute likelihood
+      for (unsigned j = 0; j < trg.size(); ++j) {
+        const WordID& f_j = trg[j];
+        double sum = 0;
+        int a_j = 0;
+        double max_pat = 0;
+        double prob_a_i = 1.0 / (src.size() + use_null);  // uniform (model 1)
+        if (use_null) {
+          if (favor_diagonal) prob_a_i = prob_align_null;
+          max_pat = s2t.prob(kNULL, f_j) * prob_a_i;
+          sum += max_pat;
+        }
+        double az = 0;
+        if (favor_diagonal)
+          az = DiagonalAlignment::ComputeZ(j+1, trg.size(), src.size(), diagonal_tension) / prob_align_not_null;
+        for (unsigned i = 1; i <= src.size(); ++i) {
+          if (favor_diagonal)
+            prob_a_i = DiagonalAlignment::UnnormalizedProb(j + 1, i, trg.size(), src.size(), diagonal_tension) / az;
+          double pat = s2t.prob(src[i-1], f_j) * prob_a_i;
+          if (pat > max_pat) { max_pat = pat; a_j = i; }
+          sum += pat;
+        }
+        log_prob += log(sum);
+        if (write_alignments) {
+          if (a_j > 0) {
+            cout << ' ';
+            if (reverse)
+              cout << j << '-' << (a_j - 1);
+            else
+              cout << (a_j - 1) << '-' << j;
+          }
+        }
+      }
+      tlp += log_prob;
+      cout << " ||| " << log_prob << endl << flush;
+    } // loop over test set sentences
+    cerr << "TOTAL LOG PROB " << tlp << endl;
+  }
+
+  if (write_alignments) return 0;
+
+  for (TTable::Word2Word2Double::iterator ei = s2t.ttable.begin(); ei != s2t.ttable.end(); ++ei) {
+    const TTable::Word2Double& cpd = ei->second;
+    const TTable::Word2Double& vit = s2t_viterbi[ei->first];
+    const string& esym = TD::Convert(ei->first);
+    double max_p = -1;
+    for (TTable::Word2Double::const_iterator fi = cpd.begin(); fi != cpd.end(); ++fi)
+      if (fi->second > max_p) max_p = fi->second;
+    const double threshold = max_p * BEAM_THRESHOLD;
+    for (TTable::Word2Double::const_iterator fi = cpd.begin(); fi != cpd.end(); ++fi) {
+      if (fi->second > threshold || (vit.find(fi->first) != vit.end())) {
+        cout << esym << ' ' << TD::Convert(fi->first) << ' ' << log(fi->second) << endl;
+      }
+    } 
+  }
+  return 0;
+}
+
diff --git a/word-aligner/makefiles/makefile.grammars b/word-aligner/makefiles/makefile.grammars
index 08ff33e1..8d3ea8cb 100644
--- a/word-aligner/makefiles/makefile.grammars
+++ b/word-aligner/makefiles/makefile.grammars
@@ -4,7 +4,7 @@ clean:
 	$(RM) orthonorm-dict.* voc2class* corpus.class.* corpus.e-f corpus.f-e corpus.f-e.lex-grammar* *.model1 *voc corpus.e-f.lex-grammar* *stem* freq* wordpairs*
 
 SUPPORT_DIR = $(SCRIPT_DIR)/support
-GZIP = /usr/bin/gzip
+GZIP = gzip
 ZCAT = zcat
 EXTRACT_GRAMMAR = $(SUPPORT_DIR)/extract_grammar.pl
 EXTRACT_VOCAB = $(SUPPORT_DIR)/extract_vocab.pl
@@ -16,7 +16,7 @@ STEM_E = $(SCRIPT_DIR)/stemmers/$(E_LANG).pl
 
 CLASSIFY = $(SUPPORT_DIR)/classify.pl
 MAKE_LEX_GRAMMAR = $(SUPPORT_DIR)/make_lex_grammar.pl
-MODEL1 = $(TRAINING_DIR)/fast_align
+MODEL1 = $(SCRIPT_DIR)/fast_align
 MERGE_CORPUS = $(SUPPORT_DIR)/merge_corpus.pl
 
 e.voc: corpus.e
diff --git a/word-aligner/makefiles/makefile.model.f-e b/word-aligner/makefiles/makefile.model.f-e
new file mode 100644
index 00000000..404f5b30
--- /dev/null
+++ b/word-aligner/makefiles/makefile.model.f-e
@@ -0,0 +1,14 @@
+all: output.f-e.aligned
+
+clean:
+	$(RM) output.f-e.a weights.cur.gz
+
+CDEC = $(SCRIPT_DIR)/../decoder/cdec
+OPTIMIZE = $(SCRIPT_DIR)/../training/crf/mpi_online_optimize
+
+weights.cur.gz: ../grammars/wordpairs.f-e.features.gz
+	$(MPIRUN) $(OPTIMIZE) -a agenda.txt -1 -C 1.0 -t ../grammars/corpus.f-e --max_walltime 90
+
+output.f-e.aligned: weights.cur.gz
+	$(CDEC) -c cdec.ini -w $< --lextrans_align_only -i ../grammars/corpus.f-e -a > $@
+
diff --git a/word-aligner/paste-parallel-files.pl b/word-aligner/paste-parallel-files.pl
deleted file mode 100755
index ce53b325..00000000
--- a/word-aligner/paste-parallel-files.pl
+++ /dev/null
@@ -1,35 +0,0 @@
-#!/usr/bin/perl -w
-use strict;
-
-my @fs = ();
-for my $file (@ARGV) {
-  my $fh;
-  open $fh, "<$file" or die "Can't open $file for reading: $!";
-  push @fs, $fh;
-}
-my $num = scalar @fs;
-die "Usage: $0 file1.txt file2.txt [...]\n" unless $num > 1;
-
-my $first = $fs[0];
-while(<$first>) {
-  chomp;
-  my @out = ();
-  push @out, $_;
-  for (my $i=1; $i < $num; $i++) {
-    my $f = $fs[$i];
-    my $line = <$f>;
-    die "Mismatched number of lines!" unless defined $line;
-    chomp $line;
-    push @out, $line;
-  }
-  print join(' ||| ', @out) . "\n";
-}
-
-for my $fh (@fs) {
-  my $x=<$fh>;
-  die "Mismatched number of lines!" if defined $x;
-  close $fh;
-}
-
-exit 0;
-
diff --git a/word-aligner/ttables.cc b/word-aligner/ttables.cc
new file mode 100644
index 00000000..45bf14c5
--- /dev/null
+++ b/word-aligner/ttables.cc
@@ -0,0 +1,31 @@
+#include "ttables.h"
+
+#include <cassert>
+
+#include "dict.h"
+
+using namespace std;
+using namespace std::tr1;
+
+void TTable::DeserializeProbsFromText(std::istream* in) {
+  int c = 0;
+  while(*in) {
+    string e;
+    string f;
+    double p;
+    (*in) >> e >> f >> p;
+    if (e.empty()) break;
+    ++c;
+    ttable[TD::Convert(e)][TD::Convert(f)] = p;
+  }
+  cerr << "Loaded " << c << " translation parameters.\n";
+}
+
+void TTable::SerializeHelper(string* out, const Word2Word2Double& o) {
+  assert(!"not implemented");
+}
+
+void TTable::DeserializeHelper(const string& in, Word2Word2Double* o) {
+  assert(!"not implemented");
+}
+
diff --git a/word-aligner/ttables.h b/word-aligner/ttables.h
new file mode 100644
index 00000000..9baa13ca
--- /dev/null
+++ b/word-aligner/ttables.h
@@ -0,0 +1,101 @@
+#ifndef _TTABLES_H_
+#define _TTABLES_H_
+
+#include <iostream>
+#include <tr1/unordered_map>
+
+#include "sparse_vector.h"
+#include "m.h"
+#include "wordid.h"
+#include "tdict.h"
+
+class TTable {
+ public:
+  TTable() {}
+  typedef std::tr1::unordered_map<WordID, double> Word2Double;
+  typedef std::tr1::unordered_map<WordID, Word2Double> Word2Word2Double;
+  inline double prob(const int& e, const int& f) const {
+    const Word2Word2Double::const_iterator cit = ttable.find(e);
+    if (cit != ttable.end()) {
+      const Word2Double& cpd = cit->second;
+      const Word2Double::const_iterator it = cpd.find(f);
+      if (it == cpd.end()) return 1e-9;
+      return it->second;
+    } else {
+      return 1e-9;
+    }
+  }
+  inline void Increment(const int& e, const int& f) {
+    counts[e][f] += 1.0;
+  }
+  inline void Increment(const int& e, const int& f, double x) {
+    counts[e][f] += x;
+  }
+  void NormalizeVB(const double alpha) {
+    ttable.swap(counts);
+    for (Word2Word2Double::iterator cit = ttable.begin();
+         cit != ttable.end(); ++cit) {
+      double tot = 0;
+      Word2Double& cpd = cit->second;
+      for (Word2Double::iterator it = cpd.begin(); it != cpd.end(); ++it)
+        tot += it->second + alpha;
+      for (Word2Double::iterator it = cpd.begin(); it != cpd.end(); ++it)
+        it->second = exp(Md::digamma(it->second + alpha) - Md::digamma(tot));
+    }
+    counts.clear();
+  }
+  void Normalize() {
+    ttable.swap(counts);
+    for (Word2Word2Double::iterator cit = ttable.begin();
+         cit != ttable.end(); ++cit) {
+      double tot = 0;
+      Word2Double& cpd = cit->second;
+      for (Word2Double::iterator it = cpd.begin(); it != cpd.end(); ++it)
+        tot += it->second;
+      for (Word2Double::iterator it = cpd.begin(); it != cpd.end(); ++it)
+        it->second /= tot;
+    }
+    counts.clear();
+  }
+  // adds counts from another TTable - probabilities remain unchanged
+  TTable& operator+=(const TTable& rhs) {
+    for (Word2Word2Double::const_iterator it = rhs.counts.begin();
+         it != rhs.counts.end(); ++it) {
+      const Word2Double& cpd = it->second;
+      Word2Double& tgt = counts[it->first];
+      for (Word2Double::const_iterator j = cpd.begin(); j != cpd.end(); ++j) {
+        tgt[j->first] += j->second;
+      }
+    }
+    return *this;
+  }
+  void ShowTTable() const {
+    for (Word2Word2Double::const_iterator it = ttable.begin(); it != ttable.end(); ++it) {
+      const Word2Double& cpd = it->second;
+      for (Word2Double::const_iterator j = cpd.begin(); j != cpd.end(); ++j) {
+        std::cerr << "P(" << TD::Convert(j->first) << '|' << TD::Convert(it->first) << ") = " << j->second << std::endl;
+      }
+    }
+  }
+  void ShowCounts() const {
+    for (Word2Word2Double::const_iterator it = counts.begin(); it != counts.end(); ++it) {
+      const Word2Double& cpd = it->second;
+      for (Word2Double::const_iterator j = cpd.begin(); j != cpd.end(); ++j) {
+        std::cerr << "c(" << TD::Convert(j->first) << '|' << TD::Convert(it->first) << ") = " << j->second << std::endl;
+      }
+    }
+  }
+  void DeserializeProbsFromText(std::istream* in);
+  void SerializeCounts(std::string* out) const { SerializeHelper(out, counts); }
+  void DeserializeCounts(const std::string& in) { DeserializeHelper(in, &counts); }
+  void SerializeProbs(std::string* out) const { SerializeHelper(out, ttable); }
+  void DeserializeProbs(const std::string& in) { DeserializeHelper(in, &ttable); }
+ private:
+  static void SerializeHelper(std::string*, const Word2Word2Double& o);
+  static void DeserializeHelper(const std::string&, Word2Word2Double* o);
+ public:
+  Word2Word2Double ttable;
+  Word2Word2Double counts;
+};
+
+#endif