From 27db9d8c05188f64c17d61c394d3dafe8b8e93d8 Mon Sep 17 00:00:00 2001
From: Chris Dyer <redpony@gmail.com>
Date: Sat, 19 Dec 2009 14:32:28 -0500
Subject: cool new alignment feature

---
 decoder/ff_wordalign.cc | 67 +++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 67 insertions(+)

(limited to 'decoder/ff_wordalign.cc')

diff --git a/decoder/ff_wordalign.cc b/decoder/ff_wordalign.cc
index a00b2c76..f07eda02 100644
--- a/decoder/ff_wordalign.cc
+++ b/decoder/ff_wordalign.cc
@@ -1,5 +1,6 @@
 #include "ff_wordalign.h"
 
+#include <sstream>
 #include <string>
 #include <cmath>
 
@@ -126,6 +127,72 @@ void MarkovJump::TraversalFeaturesImpl(const SentenceMetadata& smeta,
   }
 }
 
+// state: POS of src word used, number of trg words generated
+SourcePOSBigram::SourcePOSBigram(const std::string& param) :
+    FeatureFunction(sizeof(WordID) + sizeof(int)) {
+  cerr << "Reading source POS tags from " << param << endl;
+  ReadFile rf(param);
+  istream& in = *rf.stream();
+  while(in) {
+    string line;
+    getline(in, line);
+    if (line.empty()) continue;
+    vector<WordID> v;
+    TD::ConvertSentence(line, &v);
+    pos_.push_back(v);
+  }
+  cerr << "  (" << pos_.size() << " lines)\n";
+}
+
+void SourcePOSBigram::FireFeature(WordID left,
+                   WordID right,
+                   SparseVector<double>* features) const {
+  int& fid = fmap_[left][right];
+  if (!fid) {
+    ostringstream os;
+    os << "SP:";
+    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());
+    if (fid == 0) fid = -1;
+  }
+  if (fid < 0) return;
+  features->set_value(fid, 1.0);
+}
+
+void SourcePOSBigram::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);
+  int& out_word_count = *(static_cast<int*>(context) + 1);
+  const int arity = edge.Arity();
+  if (arity == 0) {
+    assert(smeta.GetSentenceID() < pos_.size());
+    const vector<WordID>& pos_sent = pos_[smeta.GetSentenceID()];
+    assert(edge.i_ < pos_sent.size());
+    out_context = pos_sent[edge.i_];
+    out_word_count = edge.rule_->EWords();
+    assert(out_word_count == 1); // this is only defined for lex translation!
+    // revisit this if you want to translate into null words
+  } else if (arity == 2) {
+    WordID left = *static_cast<const WordID*>(ant_contexts[0]);
+    WordID right = *static_cast<const WordID*>(ant_contexts[1]);
+    int left_wc = *(static_cast<const int*>(ant_contexts[0]) + 1);
+    int right_wc = *(static_cast<const int*>(ant_contexts[0]) + 1);
+    if (left_wc == 1 && right_wc == 1)
+      FireFeature(-1, left, features);
+    FireFeature(left, right, features);
+    out_word_count = left_wc + right_wc;
+    if (out_word_count == smeta.GetSourceLength())
+      FireFeature(right, -1, features);
+    out_context = right;
+  }
+}
+
 AlignerResults::AlignerResults(const std::string& param) :
     cur_sent_(-1),
     cur_grid_(NULL) {
-- 
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