added non-pruning intersection and a CRF tagger

- the linear-chain tagger is more of a proof of concept than a real tagger-- the context-free assumptions made in a number of places mean that the algorithms used may not be as efficient as they could be, but the model is as powerful as any CRF
- it would be easy to add latent variables or semi-CRF support (or both!)
- i've added a couple basic features that are often used for POS tagging
- non-pruning intersection is useful for lexical word alignment models and the tagger
- a sample POS tagger model will be committed later

12 files changed, 382 insertions, 21 deletions

diff --git a/decoder/Makefile.am b/decoder/Makefile.am
index f3843102..4c86ae6f 100644
--- a/decoder/Makefile.am
+++ b/decoder/Makefile.am
@@ -60,8 +60,10 @@ libcdec_a_SOURCES = \
   ff_lm.cc \
   ff_wordalign.cc \
   ff_csplit.cc \
+  ff_tagger.cc \
   freqdict.cc \
   lexcrf.cc \
+  tagger.cc \
   bottom_up_parser.cc \
   phrasebased_translator.cc \
   JSON_parser.c \
diff --git a/decoder/apply_models.cc b/decoder/apply_models.cc
index b1d002f4..a340aa1a 100644
--- a/decoder/apply_models.cc
+++ b/decoder/apply_models.cc
@@ -296,14 +296,69 @@ public:
 };
 
 struct NoPruningRescorer {
-  NoPruningRescorer(const ModelSet& m, const Hypergraph& i, Hypergraph* o) :
+  NoPruningRescorer(const ModelSet& m, const SentenceMetadata &sm, const Hypergraph& i, Hypergraph* o) :
       models(m),
+      smeta(sm),
       in(i),
-      out(*o) {
+      out(*o),
+      nodemap(i.nodes_.size()) {
     cerr << "  Rescoring forest (full intersection)\n";
   }
 
-  void RescoreNode(const int node_num, const bool is_goal) {
+  typedef unordered_map<string, int, boost::hash<string> > State2NodeIndex;
+
+  void ExpandEdge(const Hypergraph::Edge& in_edge, bool is_goal, State2NodeIndex* state2node) {
+    const int arity = in_edge.Arity();
+    Hypergraph::TailNodeVector ends(arity);
+    for (int i = 0; i < arity; ++i)
+      ends[i] = nodemap[in_edge.tail_nodes_[i]].size();
+
+    Hypergraph::TailNodeVector tail_iter(arity, 0);
+    bool done = false;
+    while (!done) {
+      Hypergraph::TailNodeVector tail(arity);
+      for (int i = 0; i < arity; ++i)
+        tail[i] = nodemap[in_edge.tail_nodes_[i]][tail_iter[i]];
+      Hypergraph::Edge* new_edge = out.AddEdge(in_edge.rule_, tail);
+      new_edge->feature_values_ = in_edge.feature_values_;
+      new_edge->i_ = in_edge.i_;
+      new_edge->j_ = in_edge.j_;
+      new_edge->prev_i_ = in_edge.prev_i_;
+      new_edge->prev_j_ = in_edge.prev_j_;
+      string head_state;
+      if (is_goal) {
+        assert(tail.size() == 1);
+        const string& ant_state = out.nodes_[tail.front()].state_;
+        models.AddFinalFeatures(ant_state, new_edge);
+      } else {
+        prob_t edge_estimate; // this is a full intersection, so we disregard this
+        models.AddFeaturesToEdge(smeta, out, new_edge, &head_state, &edge_estimate);
+      }
+      int& head_plus1 = (*state2node)[head_state];
+      if (!head_plus1) {
+        head_plus1 = out.AddNode(in_edge.rule_->GetLHS(), head_state)->id_ + 1;
+        nodemap[in_edge.head_node_].push_back(head_plus1 - 1);
+      }
+      const int head_index = head_plus1 - 1;
+      out.ConnectEdgeToHeadNode(new_edge->id_, head_index);
+
+      int ii = 0;
+      for (; ii < arity; ++ii) {
+        ++tail_iter[ii];
+        if (tail_iter[ii] < ends[ii]) break;
+        tail_iter[ii] = 0;
+      }
+      done = (ii == arity);
+    }
+  }
+
+  void ProcessOneNode(const int node_num, const bool is_goal) {
+    State2NodeIndex state2node;
+    const Hypergraph::Node& node = in.nodes_[node_num];
+    for (int i = 0; i < node.in_edges_.size(); ++i) {
+      const Hypergraph::Edge& edge = in.edges_[node.in_edges_[i]];
+      ExpandEdge(edge, is_goal, &state2node);
+    }
   }
 
   void Apply() {
@@ -316,29 +371,41 @@ struct NoPruningRescorer {
     cerr << "    ";
     for (int i = 0; i < in.nodes_.size(); ++i) {
       if (i % every == 0) cerr << '.';
-      RescoreNode(i, i == goal_id);
+      ProcessOneNode(i, i == goal_id);
     }
     cerr << endl;
   }
 
  private:
   const ModelSet& models;
+  const SentenceMetadata& smeta;
   const Hypergraph& in;
   Hypergraph& out;
+
+  vector<vector<int> > nodemap;
 };
 
 // each node in the graph has one of these, it keeps track of
 void ApplyModelSet(const Hypergraph& in,
                    const SentenceMetadata& smeta,
                    const ModelSet& models,
-                   const PruningConfiguration& config,
+                   const IntersectionConfiguration& config,
                    Hypergraph* out) {
-  int pl = config.pop_limit;
-  if (pl > 100 && in.nodes_.size() > 80000) {
-    cerr << "  Note: reducing pop_limit to " << pl << " for very large forest\n";
-    pl = 30;
+  // TODO special handling when all models are stateless
+  if (config.algorithm == 1) {
+    int pl = config.pop_limit;
+    if (pl > 100 && in.nodes_.size() > 80000) {
+      cerr << "  Note: reducing pop_limit to " << pl << " for very large forest\n";
+      pl = 30;
+    }
+    CubePruningRescorer ma(models, smeta, in, pl, out);
+    ma.Apply();
+  } else if (config.algorithm == 0) {
+    NoPruningRescorer ma(models, smeta, in, out);
+    ma.Apply();
+  } else {
+    cerr << "Don't understand intersection algorithm " << config.algorithm << endl;
+    exit(1);
   }
-  CubePruningRescorer ma(models, smeta, in, pl, out);
-  ma.Apply();
 }
 
diff --git a/decoder/apply_models.h b/decoder/apply_models.h
index 08fce037..d6d8b34a 100644
--- a/decoder/apply_models.h
+++ b/decoder/apply_models.h
@@ -5,16 +5,16 @@ struct ModelSet;
 struct Hypergraph;
 struct SentenceMetadata;
 
-struct PruningConfiguration {
+struct IntersectionConfiguration {
   const int algorithm; // 0 = full intersection, 1 = cube pruning
   const int pop_limit; // max number of pops off the heap at each node
-  explicit PruningConfiguration(int k) : algorithm(1), pop_limit(k) {}
+  IntersectionConfiguration(int alg, int k) : algorithm(alg), pop_limit(k) {}
 };
 
 void ApplyModelSet(const Hypergraph& in,
                    const SentenceMetadata& smeta,
                    const ModelSet& models,
-                   const PruningConfiguration& config,
+                   const IntersectionConfiguration& config,
                    Hypergraph* out);
 
 #endif
diff --git a/decoder/cdec.cc b/decoder/cdec.cc
index 6185c79b..c6773cce 100644
--- a/decoder/cdec.cc
+++ b/decoder/cdec.cc
@@ -17,6 +17,7 @@
 #include "filelib.h"
 #include "sampler.h"
 #include "sparse_vector.h"
+#include "tagger.h"
 #include "lexcrf.h"
 #include "csplit.h"
 #include "weights.h"
@@ -48,7 +49,7 @@ void ShowBanner() {
 void InitCommandLine(int argc, char** argv, po::variables_map* conf) {
   po::options_description opts("Configuration options");
   opts.add_options()
-        ("formalism,f",po::value<string>(),"Translation formalism; values include SCFG, FST, PB, LexCRF (lexical translation model), CSplit (compound splitting)")
+        ("formalism,f",po::value<string>(),"Decoding formalism; values include SCFG, FST, PB, LexCRF (lexical translation model), CSplit (compound splitting), Tagger (sequence labeling)")
         ("input,i",po::value<string>()->default_value("-"),"Source file")
         ("grammar,g",po::value<vector<string> >()->composing(),"Either SCFG grammar file(s) or phrase tables file(s)")
         ("weights,w",po::value<string>(),"Feature weights file")
@@ -58,16 +59,18 @@ void InitCommandLine(int argc, char** argv, po::variables_map* conf) {
 	("k_best,k",po::value<int>(),"Extract the k best derivations")
 	("unique_k_best,r", "Unique k-best translation list")
         ("aligner,a", "Run as a word/phrase aligner (src & ref required)")
+        ("intersection_strategy,I",po::value<string>()->default_value("cube_pruning"), "Intersection strategy for incorporating finite-state features; values include Cube_pruning, Full")
         ("cubepruning_pop_limit,K",po::value<int>()->default_value(200), "Max number of pops from the candidate heap at each node")
         ("goal",po::value<string>()->default_value("S"),"Goal symbol (SCFG & FST)")
         ("scfg_extra_glue_grammar", po::value<string>(), "Extra glue grammar file (Glue grammars apply when i=0 but have no other span restrictions)")
         ("scfg_no_hiero_glue_grammar,n", "No Hiero glue grammar (nb. by default the SCFG decoder adds Hiero glue rules)")
         ("scfg_default_nt,d",po::value<string>()->default_value("X"),"Default non-terminal symbol in SCFG")
         ("scfg_max_span_limit,S",po::value<int>()->default_value(10),"Maximum non-terminal span limit (except \"glue\" grammar)")
-	("show_tree_structure,T", "Show the Viterbi derivation structure")
+	("show_tree_structure", "Show the Viterbi derivation structure")
         ("show_expected_length", "Show the expected translation length under the model")
         ("show_partition,z", "Compute and show the partition (inside score)")
         ("beam_prune", po::value<double>(), "Prune paths from +LM forest")
+        ("tagger_tagset,t", po::value<string>(), "(Tagger) file containing tag set")
         ("csplit_output_plf", "(Compound splitter) Output lattice in PLF format")
         ("csplit_preserve_full_word", "(Compound splitter) Always include the unsegmented form in the output lattice")
         ("extract_rules", po::value<string>(), "Extract the rules used in translation (de-duped) to this file")
@@ -111,8 +114,8 @@ void InitCommandLine(int argc, char** argv, po::variables_map* conf) {
   }
 
   const string formalism = LowercaseString((*conf)["formalism"].as<string>());
-  if (formalism != "scfg" && formalism != "fst" && formalism != "lexcrf" && formalism != "pb" && formalism != "csplit") {
-    cerr << "Error: --formalism takes only 'scfg', 'fst', 'pb', 'csplit' or 'lexcrf'\n";
+  if (formalism != "scfg" && formalism != "fst" && formalism != "lexcrf" && formalism != "pb" && formalism != "csplit" && formalism != "tagger") {
+    cerr << "Error: --formalism takes only 'scfg', 'fst', 'pb', 'csplit', 'lexcrf', or 'tagger'\n";
     cerr << dcmdline_options << endl;
     exit(1);
   }
@@ -255,6 +258,8 @@ int main(int argc, char** argv) {
     translator.reset(new CompoundSplit(conf));
   else if (formalism == "lexcrf")
     translator.reset(new LexicalCRF(conf));
+  else if (formalism == "tagger")
+    translator.reset(new Tagger(conf));
   else
     assert(!"error");
 
@@ -285,6 +290,12 @@ int main(int argc, char** argv) {
     }
   }
   ModelSet late_models(feature_weights, late_ffs);
+  int palg = 1;
+  if (LowercaseString(conf["intersection_strategy"].as<string>()) == "full") {
+    palg = 0;
+    cerr << "Using full intersection (no pruning).\n";
+  }
+  const IntersectionConfiguration inter_conf(palg, conf["cubepruning_pop_limit"].as<int>());
 
   const int sample_max_trans = conf.count("max_translation_sample") ?
     conf["max_translation_sample"].as<int>() : 0;
@@ -374,11 +385,10 @@ int main(int argc, char** argv) {
       forest.Reweight(feature_weights);
       forest.SortInEdgesByEdgeWeights();
       Hypergraph lm_forest;
-      int cubepruning_pop_limit = conf["cubepruning_pop_limit"].as<int>();
       ApplyModelSet(forest,
                     smeta,
                     late_models,
-                    PruningConfiguration(cubepruning_pop_limit),
+                    inter_conf,
                     &lm_forest);
       forest.swap(lm_forest);
       forest.Reweight(feature_weights);
diff --git a/decoder/cdec_ff.cc b/decoder/cdec_ff.cc
index 0a4f3d5e..bb2c9d34 100644
--- a/decoder/cdec_ff.cc
+++ b/decoder/cdec_ff.cc
@@ -4,6 +4,7 @@
 #include "ff_lm.h"
 #include "ff_csplit.h"
 #include "ff_wordalign.h"
+#include "ff_tagger.h"
 #include "ff_factory.h"
 
 boost::shared_ptr<FFRegistry> global_ff_registry;
@@ -18,5 +19,7 @@ void register_feature_functions() {
   global_ff_registry->Register("AlignerResults", new FFFactory<AlignerResults>);
   global_ff_registry->Register("CSplit_BasicFeatures", new FFFactory<BasicCSplitFeatures>);
   global_ff_registry->Register("CSplit_ReverseCharLM", new FFFactory<ReverseCharLMCSplitFeature>);
+  global_ff_registry->Register("Tagger_BigramIdentity", new FFFactory<Tagger_BigramIdentity>);
+  global_ff_registry->Register("LexicalPairIdentity", new FFFactory<LexicalPairIdentity>);
 };
 
diff --git a/decoder/ff_tagger.cc b/decoder/ff_tagger.cc
new file mode 100644
index 00000000..7a9d1def
--- /dev/null
+++ b/decoder/ff_tagger.cc
@@ -0,0 +1,96 @@
+#include "ff_tagger.h"
+
+#include "tdict.h"
+#include "sentence_metadata.h"
+
+#include <sstream>
+
+using namespace std;
+
+Tagger_BigramIdentity::Tagger_BigramIdentity(const std::string& param) :
+  FeatureFunction(sizeof(WordID)) {}
+
+void Tagger_BigramIdentity::FireFeature(const WordID& left,
+                                 const WordID& right,
+                                 SparseVector<double>* features) const {
+  int& fid = fmap_[left][right];
+  if (!fid) {
+    ostringstream os;
+    if (right == 0) {
+      os << "Uni:" << TD::Convert(left);
+    } else {
+      os << "Bi:";
+      if (left < 0) { os << "BOS"; } else { os << TD::Convert(left); }
+      os << '_';
+      if (right < 0) { os << "EOS"; } else { os << TD::Convert(right); }
+    }
+    fid = FD::Convert(os.str());
+  }
+  features->set_value(fid, 1.0);
+}
+
+void Tagger_BigramIdentity::TraversalFeaturesImpl(const SentenceMetadata& smeta,
+                                     const Hypergraph::Edge& edge,
+                                     const std::vector<const void*>& ant_contexts,
+                                     SparseVector<double>* features,
+                                     SparseVector<double>* estimated_features,
+                                     void* context) const {
+  WordID& out_context = *static_cast<WordID*>(context);
+  const int arity = edge.Arity();
+  if (arity == 0) {
+    out_context = edge.rule_->e_[0];
+    FireFeature(out_context, 0, features);
+  } else if (arity == 2) {
+    WordID left = *static_cast<const WordID*>(ant_contexts[0]);
+    WordID right = *static_cast<const WordID*>(ant_contexts[1]);
+    if (edge.i_ == 0 && edge.j_ == 2)
+      FireFeature(-1, left, features);
+    FireFeature(left, right, features);
+    if (edge.i_ == 0 && edge.j_ == smeta.GetSourceLength())
+      FireFeature(right, -1, features);
+    out_context = right;
+  }
+}
+
+LexicalPairIdentity::LexicalPairIdentity(const std::string& param) {}
+
+void LexicalPairIdentity::FireFeature(WordID src,
+                                 WordID trg,
+                                 SparseVector<double>* features) const {
+  int& fid = fmap_[src][trg];
+  if (!fid) {
+    static map<WordID, WordID> escape;
+    if (escape.empty()) {
+      escape[TD::Convert("=")] = TD::Convert("__EQ");
+      escape[TD::Convert(";")] = TD::Convert("__SC");
+      escape[TD::Convert(",")] = TD::Convert("__CO");
+    }
+    if (escape.count(src)) src = escape[src];
+    if (escape.count(trg)) trg = escape[trg];
+    ostringstream os;
+    os << "Id:" << TD::Convert(src) << ':' << TD::Convert(trg);
+    fid = FD::Convert(os.str());
+  }
+  features->set_value(fid, 1.0);
+}
+
+void LexicalPairIdentity::TraversalFeaturesImpl(const SentenceMetadata& smeta,
+                                     const Hypergraph::Edge& edge,
+                                     const std::vector<const void*>& ant_contexts,
+                                     SparseVector<double>* features,
+                                     SparseVector<double>* estimated_features,
+                                     void* context) const {
+  const vector<WordID>& ew = edge.rule_->e_;
+  const vector<WordID>& fw = edge.rule_->f_;
+  for (int i = 0; i < ew.size(); ++i) {
+    const WordID& e = ew[i];
+    if (e <= 0) continue;
+    for (int j = 0; j < fw.size(); ++j) {
+      const WordID& f = fw[j];
+      if (f <= 0) continue;
+      FireFeature(f, e, features);
+    }
+  }
+}
+
+
diff --git a/decoder/ff_tagger.h b/decoder/ff_tagger.h
new file mode 100644
index 00000000..41c3ee5b
--- /dev/null
+++ b/decoder/ff_tagger.h
@@ -0,0 +1,51 @@
+#ifndef _FF_TAGGER_H_
+#define _FF_TAGGER_H_
+
+#include <map>
+#include "ff.h"
+
+typedef std::map<WordID, int> Class2FID;
+typedef std::map<WordID, Class2FID> Class2Class2FID;
+
+// the reason this is a "tagger" feature is that it assumes that
+// the sequence unfolds from left to right, which means it doesn't
+// have to split states based on left context.
+// fires unigram features as well
+class Tagger_BigramIdentity : public FeatureFunction {
+ public:
+  Tagger_BigramIdentity(const std::string& param);
+ protected:
+  virtual void TraversalFeaturesImpl(const SentenceMetadata& smeta,
+                                     const Hypergraph::Edge& edge,
+                                     const std::vector<const void*>& ant_contexts,
+                                     SparseVector<double>* features,
+                                     SparseVector<double>* estimated_features,
+                                     void* context) const;
+ private:
+  void FireFeature(const WordID& left,
+                   const WordID& right,
+                   SparseVector<double>* features) const;
+  mutable Class2Class2FID fmap_;
+};
+
+// for each pair of symbols cooccuring in a lexicalized rule, fire
+// a feature (mostly used for tagging, but could be used for any model)
+class LexicalPairIdentity : public FeatureFunction {
+ public:
+  LexicalPairIdentity(const std::string& param);
+ protected:
+  virtual void TraversalFeaturesImpl(const SentenceMetadata& smeta,
+                                     const Hypergraph::Edge& edge,
+                                     const std::vector<const void*>& ant_contexts,
+                                     SparseVector<double>* features,
+                                     SparseVector<double>* estimated_features,
+                                     void* context) const;
+ private:
+  void FireFeature(WordID src,
+                   WordID trg,
+                   SparseVector<double>* features) const;
+  mutable Class2Class2FID fmap_;
+};
+
+
+#endif
diff --git a/decoder/lexcrf.cc b/decoder/lexcrf.cc
index 33455a3d..816506e4 100644
--- a/decoder/lexcrf.cc
+++ b/decoder/lexcrf.cc
@@ -81,7 +81,7 @@ struct LexicalCRFImpl {
       }
     }
     Hypergraph::TailNodeVector tail(1, forest->nodes_.size() - 1);
-    Hypergraph::Node* goal = forest->AddNode(TD::Convert("[Goal]")*-1);
+    Hypergraph::Node* goal = forest->AddNode(TD::Convert("Goal")*-1);
     Hypergraph::Edge* hg_edge = forest->AddEdge(kGOAL_RULE, tail);
     forest->ConnectEdgeToHeadNode(hg_edge, goal);
   }
diff --git a/decoder/tagger.cc b/decoder/tagger.cc
new file mode 100644
index 00000000..5a0155cc
--- /dev/null
+++ b/decoder/tagger.cc
@@ -0,0 +1,109 @@
+#include "tagger.h"
+
+#include "tdict.h"
+#include "hg_io.h"
+#include "filelib.h"
+#include "hg.h"
+#include "wordid.h"
+#include "sentence_metadata.h"
+
+using namespace std;
+
+// This is a really simple linear chain tagger.
+// You specify a tagset, and it hypothesizes that each word in the
+// input can be tagged with any member of the tagset.
+// The are a couple sample features implemented in ff_tagger.h/cc
+// One thing to note, that while CRFs typically define the label
+// sequence as corresponding to the hidden states in a trellis,
+// in our model the labels are on edges, but mathematically
+// they are identical.
+//
+// Things to do if you want to make this a "real" tagger:
+// - support dictionaries (for each word, limit the tags considered)
+// - add latent variables - this is really easy to do
+
+static void ReadTagset(const string& file, vector<WordID>* tags) {
+  ReadFile rf(file);
+  istream& in(*rf.stream());
+  while(in) {
+    string tag;
+    in >> tag;
+    if (tag.empty()) continue;
+    tags->push_back(TD::Convert(tag));
+  }
+  cerr << "Read " << tags->size() << " labels (tags) from " << file << endl;
+}
+
+struct TaggerImpl {
+  TaggerImpl(const boost::program_options::variables_map& conf) :
+      kXCAT(TD::Convert("X")*-1),
+      kNULL(TD::Convert("<eps>")),
+      kBINARY(new TRule("[X] ||| [X,1] [X,2] ||| [1] [2]")),
+      kGOAL_RULE(new TRule("[Goal] ||| [X,1] ||| [1]")) {
+    if (conf.count("tagger_tagset") == 0) {
+      cerr << "Tagger requires --tagger_tagset FILE!\n";
+      exit(1);
+    }
+    ReadTagset(conf["tagger_tagset"].as<string>(), &tagset_);
+  }
+
+  void BuildTrellis(const vector<WordID>& seq, Hypergraph* forest) {
+    int prev_node_id = -1;
+    for (int i = 0; i < seq.size(); ++i) {
+      const WordID& src = seq[i];
+      const int new_node_id = forest->AddNode(kXCAT)->id_;
+      for (int k = 0; k < tagset_.size(); ++k) {
+        TRulePtr rule(TRule::CreateLexicalRule(src, tagset_[k]));
+        Hypergraph::Edge* edge = forest->AddEdge(rule, Hypergraph::TailNodeVector());
+        edge->i_ = i;
+        edge->j_ = i+1;
+        forest->ConnectEdgeToHeadNode(edge->id_, new_node_id);
+      }
+      if (prev_node_id >= 0) {
+        const int comb_node_id = forest->AddNode(kXCAT)->id_;
+        Hypergraph::TailNodeVector tail(2, prev_node_id);
+        tail[1] = new_node_id;
+        Hypergraph::Edge* edge = forest->AddEdge(kBINARY, tail);
+        edge->i_ = 0;
+        edge->j_ = i+1;
+        forest->ConnectEdgeToHeadNode(edge->id_, comb_node_id);
+        prev_node_id = comb_node_id;
+      } else {
+        prev_node_id = new_node_id;
+      }
+    }
+    Hypergraph::TailNodeVector tail(1, forest->nodes_.size() - 1);
+    Hypergraph::Node* goal = forest->AddNode(TD::Convert("Goal")*-1);
+    Hypergraph::Edge* hg_edge = forest->AddEdge(kGOAL_RULE, tail);
+    forest->ConnectEdgeToHeadNode(hg_edge, goal);
+  }
+
+ private:
+  vector<WordID> tagset_;
+  const WordID kXCAT;
+  const WordID kNULL;
+  const TRulePtr kBINARY;
+  const TRulePtr kGOAL_RULE;
+};
+
+Tagger::Tagger(const boost::program_options::variables_map& conf) :
+ pimpl_(new TaggerImpl(conf)) {}
+
+
+bool Tagger::Translate(const string& input,
+                       SentenceMetadata* smeta,
+                       const vector<double>& weights,
+                       Hypergraph* forest) {
+  Lattice lattice;
+  LatticeTools::ConvertTextToLattice(input, &lattice);
+  smeta->SetSourceLength(lattice.size());
+  vector<WordID> sequence(lattice.size());
+  for (int i = 0; i < lattice.size(); ++i) {
+    assert(lattice[i].size() == 1);
+    sequence[i] = lattice[i][0].label;
+  }
+  pimpl_->BuildTrellis(sequence, forest);
+  forest->Reweight(weights);
+  return true;
+}
+
diff --git a/decoder/tagger.h b/decoder/tagger.h
new file mode 100644
index 00000000..900019f2
--- /dev/null
+++ b/decoder/tagger.h
@@ -0,0 +1,17 @@
+#ifndef _TAGGER_H_
+#define _TAGGER_H_
+
+#include "translator.h"
+
+struct TaggerImpl;
+struct Tagger : public Translator {
+  Tagger(const boost::program_options::variables_map& conf);
+  bool Translate(const std::string& input,
+                 SentenceMetadata* smeta,
+                 const std::vector<double>& weights,
+                 Hypergraph* forest);
+ private:
+  boost::shared_ptr<TaggerImpl> pimpl_;
+};
+
+#endif
diff --git a/decoder/trule.h b/decoder/trule.h
index d2b1babe..42edfa2c 100644
--- a/decoder/trule.h
+++ b/decoder/trule.h
@@ -39,6 +39,10 @@ class TRule {
   //    [LHS] ||| term1 [NT] term2 [OTHER_NT] [YET_ANOTHER_NT]
   static TRule* CreateRuleMonolingual(const std::string& rule);
 
+  static TRule* CreateLexicalRule(const WordID& src, const WordID& trg) {
+    return new TRule(src, trg);
+  }
+
   void ESubstitute(const std::vector<const std::vector<WordID>* >& var_values,
                    std::vector<WordID>* result) const {
     int vc = 0;
@@ -116,6 +120,7 @@ class TRule {
   short int prev_j;
 
  private:
+  TRule(const WordID& src, const WordID& trg) : e_(1, trg), f_(1, src), lhs_(), arity_(), prev_i(), prev_j() {}
   bool SanityCheck() const;
 };
 
diff --git a/training/cluster-ptrain.pl b/training/cluster-ptrain.pl
index 8b06f162..33aab25d 100755
--- a/training/cluster-ptrain.pl
+++ b/training/cluster-ptrain.pl
@@ -36,6 +36,7 @@ GetOptions("cdec=s" => \$DECODER,
            "sigma_squared=f" => \$sigsq,
            "means=s" => \$means_file,
            "optimizer=s" => \$OALG,
+           "gaussian_prior" => \$PRIOR,
            "jobs=i" => \$nodes,
            "pmem=s" => \$pmem
           ) or usage();