added Gesa's parse match feature function

4 files changed, 247 insertions, 0 deletions

diff --git a/decoder/Makefile.am b/decoder/Makefile.am
index 82b50f19..e075fe37 100644
--- a/decoder/Makefile.am
+++ b/decoder/Makefile.am
@@ -143,6 +143,8 @@ libcdec_a_SOURCES = \
   ff_tagger.cc \
   ff_source_path.cc \
   ff_source_syntax.cc \
+	ff_parse_match.cc \
+	ff_parse_match.h \
   ff_bleu.cc \
   ff_factory.cc \
   incremental.cc \
diff --git a/decoder/cdec_ff.cc b/decoder/cdec_ff.cc
index 0bf441d4..afa421ae 100644
--- a/decoder/cdec_ff.cc
+++ b/decoder/cdec_ff.cc
@@ -16,6 +16,7 @@
 #include "ff_bleu.h"
 #include "ff_source_path.h"
 #include "ff_source_syntax.h"
+#include "ff_parse_match.h"
 #include "ff_register.h"
 #include "ff_charset.h"
 #include "ff_wordset.h"
@@ -49,6 +50,7 @@ void register_feature_functions() {
   ff_registry.Register("RuleContextFeatures", new FFFactory<RuleContextFeatures>());
   ff_registry.Register("RuleIdentityFeatures", new FFFactory<RuleIdentityFeatures>());
   ff_registry.Register("SourceSyntaxFeatures", new FFFactory<SourceSyntaxFeatures>);
+  ff_registry.Register("ParseMatchFeatures", new FFFactory<ParseMatchFeatures>);
   ff_registry.Register("SourceSpanSizeFeatures", new FFFactory<SourceSpanSizeFeatures>);
   ff_registry.Register("CMR2008ReorderingFeatures", new FFFactory<CMR2008ReorderingFeatures>());
   ff_registry.Register("RuleSourceBigramFeatures", new FFFactory<RuleSourceBigramFeatures>());
diff --git a/decoder/ff_parse_match.cc b/decoder/ff_parse_match.cc
new file mode 100644
index 00000000..ed556b91
--- /dev/null
+++ b/decoder/ff_parse_match.cc
@@ -0,0 +1,218 @@
+#include "ff_parse_match.h"
+
+#include <sstream>
+#include <stack>
+#include <string>
+
+#include "sentence_metadata.h"
+#include "array2d.h"
+#include "filelib.h"
+
+using namespace std;
+
+// implements the parse match features as described in Vilar et al. (2008)
+// source trees must be represented in Penn Treebank format, e.g.
+//     (S (NP John) (VP (V left)))
+
+struct ParseMatchFeaturesImpl {
+  ParseMatchFeaturesImpl(const string& param) {
+    if (param.compare("") != 0) {
+      char score_param = (char) param[0];
+      switch(score_param) {
+        case 'b':
+          scoring_method = 0;
+          break;
+        case 'l':
+          scoring_method = 1;
+          break;
+        case 'e':
+          scoring_method = 2;
+          break;
+        case 'r':
+          scoring_method = 3;
+          break;
+        default:
+          scoring_method = 0;
+      }
+    }
+    else {
+      scoring_method = 0;
+    }
+  }
+
+  void InitializeGrids(const string& tree, unsigned src_len) {
+    assert(tree.size() > 0);
+    //fids_cat.clear();
+    fids_ef.clear();
+    src_tree.clear();
+    //fids_cat.resize(src_len, src_len + 1);
+    fids_ef.resize(src_len, src_len + 1);
+    src_tree.resize(src_len, src_len + 1, TD::Convert("XX"));
+    ParseTreeString(tree, src_len);
+  }
+
+  void ParseTreeString(const string& tree, unsigned src_len) {
+    //cerr << "TREE: " << tree << endl;
+    src_sent_len = src_len;
+    stack<pair<int, WordID> > stk;  // first = i, second = category
+    pair<int, WordID> cur_cat; cur_cat.first = -1;
+    unsigned i = 0;
+    unsigned p = 0;
+    while(p < tree.size()) {
+      const char cur = tree[p];
+      if (cur == '(') {
+        stk.push(cur_cat);
+        ++p;
+        unsigned k = p + 1;
+        while (k < tree.size() && tree[k] != ' ') { ++k; }
+        cur_cat.first = i;
+        cur_cat.second = TD::Convert(tree.substr(p, k - p));
+        // cerr << "NT: '" << tree.substr(p, k-p) << "' (i=" << i << ")\n";
+        p = k + 1;
+      } else if (cur == ')') {
+        unsigned k = p;
+        while (k < tree.size() && tree[k] == ')') { ++k; }
+        const unsigned num_closes = k - p;
+        for (unsigned ci = 0; ci < num_closes; ++ci) {
+          // cur_cat.second spans from cur_cat.first to i
+          // cerr << TD::Convert(cur_cat.second) << " from " << cur_cat.first << " to " << i << endl;
+          // NOTE: unary rule chains end up being labeled with the top-most category
+          src_tree(cur_cat.first, i) = cur_cat.second;
+          cur_cat = stk.top();
+          stk.pop();
+        }
+        p = k;
+        while (p < tree.size() && (tree[p] == ' ' || tree[p] == '\t')) { ++p; }
+      } else if (cur == ' ' || cur == '\t') {
+        cerr << "Unexpected whitespace in: " << tree << endl;
+        abort();
+      } else { // terminal symbol
+        unsigned k = p + 1;
+        do {
+          while (k < tree.size() && tree[k] != ')' && tree[k] != ' ') { ++k; }
+          // cerr << "TERM: '" << tree.substr(p, k-p) << "' (i=" << i << ")\n";
+          ++i;
+          assert(i <= src_len);
+          while (k < tree.size() && tree[k] == ' ') { ++k; }
+          p = k;
+        } while (p < tree.size() && tree[p] != ')');
+      }
+    //cerr << "i=" << i << "  src_len=" << src_len << endl;
+    }
+    //cerr << "i=" << i << "  src_len=" << src_len << endl;
+    assert(i == src_len);  // make sure tree specified in src_tree is
+                           // the same length as the source sentence
+  }
+
+  int FireFeatures(const TRule& rule, const int i, const int j, int* ants, SparseVector<double>* feats) {
+    //cerr << "fire features: " << rule.AsString() << " for " << i << "," << j << endl;
+    //cerr << rule << endl;
+    //cerr << "span: " << i << " " << j << endl;
+    const WordID lhs = src_tree(i,j);
+    int fid_ef = FD::Convert("PM");
+    int min_dist; // minimal distance to next syntactic constituent of this rule's LHS
+    int summed_min_dists; // minimal distances of LHS and NTs summed up
+    if (TD::Convert(lhs).compare("XX") != 0) 
+        min_dist= 0;
+    // compute the distance to the next syntactical constituent
+    else {
+      int ok = 0;
+      for (unsigned int k = 1; k < (j - i); k++) {
+        min_dist = k;
+        for (unsigned int l = 0; l <= k; l++) {
+          // check if adding k words to the rule span will
+          // lead to a syntactical constituent
+          int l_add = i-l;
+          int r_add = j+(k-l);
+          //cerr << "Adding: " << l_add << " " << r_add << endl;
+          if ((l_add < src_tree.width() && r_add < src_tree.height()) && (TD::Convert(src_tree(l_add, r_add)).compare("XX") != 0)) {
+            //cerr << TD::Convert(src_tree(i-l,j+(k-l))) << endl;
+            //cerr << "span_add: " << l_add << " " << r_add << endl;
+            ok = 1;
+            break;
+          }
+          // check if removing k words from the rule span will 
+          // lead to a syntactical constituent
+          else {
+            //cerr << "Hilfe...!" << endl;
+            int l_rem= i+l;
+            int r_rem = j-(k-l);
+            //cerr << "Removing: " << l_rem << " " << r_rem << endl;
+            if ((l_rem < src_tree.width() && r_rem < src_tree.height()) && TD::Convert(src_tree(l_rem, r_rem)).compare("XX") != 0) {
+              //cerr << TD::Convert(src_tree(i+l,j-(k-l))) << endl;
+              //cerr << "span_rem: " << l_rem << " " << r_rem << endl;
+              ok = 1;
+              break;
+            }
+          }    
+        }
+        if (ok) break;
+      }
+    }
+    summed_min_dists = min_dist;
+    //cerr << min_dist << endl;
+    unsigned ntc = 0;
+    for (unsigned k = 0; k < rule.f_.size(); ++k) {
+      int fj = rule.f_[k];
+      if (fj <= 0)
+        summed_min_dists += ants[ntc++];
+    }
+    switch(scoring_method) {
+      case 0:
+        // binary scoring
+        feats->set_value(fid_ef, (summed_min_dists == 0));
+        break;
+      // CHECK: for the remaining scoring methods, the question remains if
+      // min_dist or summed_min_dists should be used
+      case 1:
+        // linear scoring
+        feats->set_value(fid_ef, 1.0/(min_dist+1));
+        break;
+      case 2:
+        // exponential scoring
+        feats->set_value(fid_ef, 1.0/exp(min_dist));
+        break;
+      case 3:
+        // relative scoring
+        feats->set_value(fid_ef, (j-i)/((j-i) + min_dist));
+        break;
+      default:
+        // binary scoring in case nothing is defined
+        feats->set_value(fid_ef, (summed_min_dists == 0));
+    }
+    return min_dist;
+  }
+
+  Array2D<WordID> src_tree;  // src_tree(i,j) NT = type
+  unsigned int src_sent_len;
+  mutable Array2D<map<const TRule*, int> > fids_ef;    // fires for fully lexicalized
+  int scoring_method;
+};
+
+ParseMatchFeatures::ParseMatchFeatures(const string& param) :
+    FeatureFunction(sizeof(WordID)) {
+  impl = new ParseMatchFeaturesImpl(param);
+}
+
+ParseMatchFeatures::~ParseMatchFeatures() {
+  delete impl;
+  impl = NULL;
+}
+
+void ParseMatchFeatures::TraversalFeaturesImpl(const SentenceMetadata& smeta,
+                                     const Hypergraph::Edge& edge,
+                                     const vector<const void*>& ant_contexts,
+                                     SparseVector<double>* features,
+                                     SparseVector<double>* estimated_features,
+                                     void* context) const {
+  int ants[8];
+  for (unsigned i = 0; i < ant_contexts.size(); ++i)
+    ants[i] = *static_cast<const int*>(ant_contexts[i]);
+
+  *static_cast<int*>(context) =
+     impl->FireFeatures(*edge.rule_, edge.i_, edge.j_, ants, features);
+}
+
+void ParseMatchFeatures::PrepareForInput(const SentenceMetadata& smeta) {
+  impl->InitializeGrids(smeta.GetSGMLValue("src_tree"), smeta.GetSourceLength());
+}
diff --git a/decoder/ff_parse_match.h b/decoder/ff_parse_match.h
new file mode 100644
index 00000000..fa73481a
--- /dev/null
+++ b/decoder/ff_parse_match.h
@@ -0,0 +1,25 @@
+#ifndef _FF_PARSE_MATCH_H_
+#define _FF_PARSE_MATCH_H_
+
+#include "ff.h"
+#include "hg.h"
+
+struct ParseMatchFeaturesImpl;
+
+class ParseMatchFeatures : public FeatureFunction {
+ public:
+  ParseMatchFeatures(const std::string& param);
+  ~ParseMatchFeatures();
+ 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;
+  virtual void PrepareForInput(const SentenceMetadata& smeta);
+ private:
+  ParseMatchFeaturesImpl* impl;
+};
+
+#endif