1 files changed, 11 insertions, 10 deletions

diff --git a/mteval/mbr_kbest.cc b/mteval/mbr_kbest.cc
index 64a6a8bf..b5e4750c 100644
--- a/mteval/mbr_kbest.cc
+++ b/mteval/mbr_kbest.cc
@@ -5,7 +5,7 @@
 
 #include "prob.h"
 #include "tdict.h"
-#include "scorer.h"
+#include "ns.h"
 #include "filelib.h"
 #include "stringlib.h"
 
@@ -17,7 +17,7 @@ void InitCommandLine(int argc, char** argv, po::variables_map* conf) {
   po::options_description opts("Configuration options");
   opts.add_options()
         ("scale,a",po::value<double>()->default_value(1.0), "Posterior scaling factor (alpha)")
-        ("loss_function,l",po::value<string>()->default_value("bleu"), "Loss function")
+        ("evaluation_metric,m",po::value<string>()->default_value("ibm_bleu"), "Evaluation metric")
         ("input,i",po::value<string>()->default_value("-"), "File to read k-best lists from")
         ("output_list,L", "Show reranked list as output")
         ("help,h", "Help");
@@ -75,13 +75,14 @@ bool ReadKBestList(istream* in, string* sent_id, vector<pair<vector<WordID>, pro
 int main(int argc, char** argv) {
   po::variables_map conf;
   InitCommandLine(argc, argv, &conf);
-  const string metric = conf["loss_function"].as<string>();
+  const string smetric = conf["evaluation_metric"].as<string>();
+  EvaluationMetric* metric = EvaluationMetric::Instance(smetric);
+  const bool is_loss = (UppercaseString(smetric) == "TER");
   const bool output_list = conf.count("output_list") > 0;
   const string file = conf["input"].as<string>();
   const double mbr_scale = conf["scale"].as<double>();
   cerr << "Posterior scaling factor (alpha) = " << mbr_scale << endl;
 
-  ScoreType type = ScoreTypeFromString(metric);
   vector<pair<vector<WordID>, prob_t> > list;
   ReadFile rf(file);
   string sent_id;
@@ -99,15 +100,15 @@ int main(int argc, char** argv) {
     vector<double> mbr_scores(output_list ? list.size() : 0);
     double mbr_loss = numeric_limits<double>::max();
     for (int i = 0 ; i < list.size(); ++i) {
-      vector<vector<WordID> > refs(1, list[i].first);
-      //cerr << i << ": " << list[i].second <<"\t" << TD::GetString(list[i].first) << endl;
-      ScorerP scorer = SentenceScorer::CreateSentenceScorer(type, refs);
+      const vector<vector<WordID> > refs(1, list[i].first);
+
       double wl_acc = 0;
       for (int j = 0; j < list.size(); ++j) {
         if (i != j) {
-          ScoreP s = scorer->ScoreCandidate(list[j].first);
-          double loss = 1.0 - s->ComputeScore();
-          if (type == TER || type == AER) loss = 1.0 - loss;
+          SufficientStats ss;
+          metric->ComputeSufficientStatistics(list[j].first, refs, &ss);
+          double loss = 1.0 - metric->ComputeScore(ss);
+          if (is_loss) loss = 1.0 - loss;
           double weighted_loss = loss * (joints[j] / marginal).as_float();
           wl_acc += weighted_loss;
           if ((!output_list) && wl_acc > mbr_loss) break;