diff --git a/mert/BleuScorer.cpp b/mert/BleuScorer.cpp
index 381abeabf..04f4d75c9 100644
--- a/mert/BleuScorer.cpp
+++ b/mert/BleuScorer.cpp
@@ -289,23 +289,6 @@ float sentenceLevelBackgroundBleu(const std::vector<float>& sent, const std::vec
   return exp(logbleu) * stats[kBleuNgramOrder*2];
 }
 
-float unsmoothedBleu(const std::vector<float>& stats)
-{
-  UTIL_THROW_IF(stats.size() != kBleuNgramOrder * 2 + 1, util::Exception, "Error");
-
-  float logbleu = 0.0;
-  for (int j = 0; j < kBleuNgramOrder; j++) {
-    logbleu += log(stats[2 * j]) - log(stats[2 * j + 1]);
-  }
-  logbleu /= kBleuNgramOrder;
-  const float brevity = 1.0 - stats[(kBleuNgramOrder * 2)] / stats[1];
-
-  if (brevity < 0.0) {
-    logbleu += brevity;
-  }
-  return exp(logbleu);
-}
-
 vector<float> BleuScorer::ScoreNbestList(const string& scoreFile, const string& featureFile)
 {
   vector<string> scoreFiles;
diff --git a/mert/BleuScorer.h b/mert/BleuScorer.h
index 5fdce4a94..0594c8dca 100644
--- a/mert/BleuScorer.h
+++ b/mert/BleuScorer.h
@@ -55,6 +55,10 @@ public:
     return m_references.get();
   }
 
+  virtual float getReferenceLength(const std::vector<ScoreStatsType>& totals) const {
+    return totals[kBleuNgramOrder*2];
+  }
+
   /**
    * Count the ngrams of each type, up to the given length in the input line.
    */
@@ -93,11 +97,6 @@ float smoothedSentenceBleu
  */
 float sentenceLevelBackgroundBleu(const std::vector<float>& sent, const std::vector<float>& bg);
 
-/**
- * Computes plain old BLEU from a vector of stats
- */
-float unsmoothedBleu(const std::vector<float>& stats);
-
 }
 
 #endif  // MERT_BLEU_SCORER_H_
diff --git a/mert/HopeFearDecoder.cpp b/mert/HopeFearDecoder.cpp
index 23ba1e099..d54d29936 100644
--- a/mert/HopeFearDecoder.cpp
+++ b/mert/HopeFearDecoder.cpp
@@ -28,7 +28,7 @@ Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301  USA
 #include "util/exception.hh"
 #include "util/file_piece.hh"
 
-#include "BleuScorer.h"
+#include "Scorer.h"
 #include "HopeFearDecoder.h"
 
 using namespace std;
@@ -39,7 +39,7 @@ namespace MosesTuning {
 static const ValType BLEU_RATIO = 5;
 
 ValType HopeFearDecoder::Evaluate(const AvgWeightVector& wv) {
-  vector<ValType> stats(kBleuNgramOrder*2+1,0);
+  vector<ValType> stats(scorer_->NumberOfScores(),0);
   for(reset(); !finished(); next()) {
     vector<ValType> sent;
     MaxModel(wv,&sent);
@@ -47,7 +47,7 @@ ValType HopeFearDecoder::Evaluate(const AvgWeightVector& wv) {
       stats[i]+=sent[i];
     }
   }
-  return unsmoothedBleu(stats);
+  return scorer_->calculateScore(stats);
 }
 
 NbestHopeFearDecoder::NbestHopeFearDecoder(
@@ -55,8 +55,10 @@ NbestHopeFearDecoder::NbestHopeFearDecoder(
       const vector<string>&  scoreFiles,
       bool streaming,
       bool  no_shuffle,
-      bool safe_hope
+      bool safe_hope,
+      Scorer* scorer
       ) : safe_hope_(safe_hope) {
+  scorer_ = scorer;
   if (streaming) {
     train_.reset(new StreamingHypPackEnumerator(featureFiles, scoreFiles));
   } else {
@@ -93,7 +95,7 @@ void NbestHopeFearDecoder::HopeFear(
     for(size_t i=0; i< train_->cur_size(); i++) {
       const MiraFeatureVector& vec=train_->featuresAt(i);
       ValType score = wv.score(vec);
-      ValType bleu = sentenceLevelBackgroundBleu(train_->scoresAt(i),backgroundBleu);
+      ValType bleu = scorer_->calculateSentenceLevelBackgroundScore(train_->scoresAt(i),backgroundBleu);
       // Hope
       if(i==0 || (hope_scale*score + bleu) > hope_score) {
         hope_score = hope_scale*score + bleu;
@@ -124,9 +126,9 @@ void NbestHopeFearDecoder::HopeFear(
   hopeFear->fearFeatures = train_->featuresAt(fear_index);
 
   hopeFear->hopeStats = train_->scoresAt(hope_index);
-  hopeFear->hopeBleu = sentenceLevelBackgroundBleu(hopeFear->hopeStats, backgroundBleu);
+  hopeFear->hopeBleu = scorer_->calculateSentenceLevelBackgroundScore(hopeFear->hopeStats, backgroundBleu);
   const vector<float>& fear_stats = train_->scoresAt(fear_index);
-  hopeFear->fearBleu = sentenceLevelBackgroundBleu(fear_stats, backgroundBleu);
+  hopeFear->fearBleu = scorer_->calculateSentenceLevelBackgroundScore(fear_stats, backgroundBleu);
 
   hopeFear->modelStats = train_->scoresAt(model_index);
   hopeFear->hopeFearEqual = (hope_index == fear_index);
@@ -158,7 +160,8 @@ HypergraphHopeFearDecoder::HypergraphHopeFearDecoder
                             bool no_shuffle,
                             bool safe_hope,
                             size_t hg_pruning,
-                            const MiraWeightVector& wv
+                            const MiraWeightVector& wv,
+                            Scorer* scorer
                           ) :
                           num_dense_(num_dense) {
 
@@ -169,6 +172,7 @@ HypergraphHopeFearDecoder::HypergraphHopeFearDecoder
 
   SparseVector weights;
   wv.ToSparse(&weights);
+  scorer_ = scorer;
 
   static const string kWeights = "weights";
   fs::directory_iterator dend;
@@ -260,9 +264,9 @@ void HypergraphHopeFearDecoder::HopeFear(
 
   //Only C++11
   //hopeFear->modelStats.assign(std::begin(modelHypo.bleuStats), std::end(modelHypo.bleuStats));
-  vector<ValType> fearStats(kBleuNgramOrder*2+1);
-  hopeFear->hopeStats.reserve(kBleuNgramOrder*2+1);
-  hopeFear->modelStats.reserve(kBleuNgramOrder*2+1);
+  vector<ValType> fearStats(scorer_->NumberOfScores());
+  hopeFear->hopeStats.reserve(scorer_->NumberOfScores());
+  hopeFear->modelStats.reserve(scorer_->NumberOfScores());
   for (size_t i = 0; i < fearStats.size(); ++i) {
     hopeFear->modelStats.push_back(modelHypo.bleuStats[i]);
     hopeFear->hopeStats.push_back(hopeHypo.bleuStats[i]);
@@ -320,7 +324,7 @@ void HypergraphHopeFearDecoder::MaxModel(const AvgWeightVector& wv, vector<ValTy
   size_t sentenceId = *sentenceIdIter_;
   SparseVector weights;
   wv.ToSparse(&weights);
-  vector<ValType> bg(kBleuNgramOrder*2+1);
+  vector<ValType> bg(scorer_->NumberOfScores());
   Viterbi(*(graphs_[sentenceId]), weights, 0, references_, sentenceId, bg, &bestHypo);
   stats->resize(bestHypo.bleuStats.size());
   /*
diff --git a/mert/HopeFearDecoder.h b/mert/HopeFearDecoder.h
index 694a3217e..d1881eeb2 100644
--- a/mert/HopeFearDecoder.h
+++ b/mert/HopeFearDecoder.h
@@ -37,6 +37,8 @@ Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301  USA
 
 namespace MosesTuning {
 
+class Scorer;
+
 /** To be filled in by the decoder */
 struct HopeFearData {
   MiraFeatureVector modelFeatures;
@@ -60,6 +62,8 @@ public:
   virtual void next() = 0;
   virtual bool finished() = 0;
 
+  virtual ~HopeFearDecoder() {};
+
   /**
     * Calculate hope, fear and model hypotheses
     **/
@@ -76,6 +80,8 @@ public:
   /** Calculate bleu on training set */
   ValType Evaluate(const AvgWeightVector& wv);
 
+protected:
+  Scorer* scorer_;
 };
 
 
@@ -86,7 +92,8 @@ public:
                          const std::vector<std::string>&  scoreFiles,
                          bool streaming,
                          bool  no_shuffle,
-                         bool safe_hope
+                         bool safe_hope,
+                         Scorer* scorer
                          );
 
   virtual void reset();
@@ -120,7 +127,8 @@ public:
                             bool no_shuffle,
                             bool safe_hope,
                             size_t hg_pruning,
-                            const MiraWeightVector& wv
+                            const MiraWeightVector& wv,
+                            Scorer* scorer_
                             );
 
   virtual void reset();
diff --git a/mert/HwcmScorer.h b/mert/HwcmScorer.h
index 1392143d5..16d563424 100644
--- a/mert/HwcmScorer.h
+++ b/mert/HwcmScorer.h
@@ -35,6 +35,10 @@ public:
 
   virtual float calculateScore(const std::vector<ScoreStatsType>& comps) const;
 
+  virtual float getReferenceLength(const std::vector<ScoreStatsType>& totals) const {
+    return totals[2];
+  }
+
   //TODO: actually, we use trees which we store in place of alignment. Maybe use something analogous to Phrase Properties to cleanly store trees?
   bool useAlignment() const {
     return true;
diff --git a/mert/InterpolatedScorer.cpp b/mert/InterpolatedScorer.cpp
index 87cec9211..ea4240472 100644
--- a/mert/InterpolatedScorer.cpp
+++ b/mert/InterpolatedScorer.cpp
@@ -153,6 +153,41 @@ void InterpolatedScorer::score(const candidates_t& candidates, const diffs_t& di
 
 }
 
+/** Interpolated scorer gets a vector of sufficient statistics, calls all scorers with corresponding statistics,
+    and combines them with weights **/
+float InterpolatedScorer::calculateScore(const std::vector<ScoreStatsType>& totals) const
+{
+  size_t scorerNum = 0;
+  size_t last = 0;
+  float score = 0;
+  for (ScopedVector<Scorer>::const_iterator itsc = m_scorers.begin();
+       itsc != m_scorers.end(); ++itsc) {
+    int numScoresScorer = (*itsc)->NumberOfScores();
+    std::vector<ScoreStatsType> totals_scorer(totals.begin()+last, totals.begin()+last+numScoresScorer);
+    score += (*itsc)->calculateScore(totals_scorer) * m_scorer_weights[scorerNum];
+    last += numScoresScorer;
+    scorerNum++;
+  }
+  return score;
+}
+
+
+float InterpolatedScorer::getReferenceLength(const std::vector<ScoreStatsType>& totals) const
+{
+    size_t scorerNum = 0;
+    size_t last = 0;
+    float refLen = 0;
+    for (ScopedVector<Scorer>::const_iterator itsc = m_scorers.begin();
+         itsc != m_scorers.end(); ++itsc) {
+      int numScoresScorer = (*itsc)->NumberOfScores();
+      std::vector<ScoreStatsType> totals_scorer(totals.begin()+last, totals.begin()+last+numScoresScorer);
+      refLen += (*itsc)->getReferenceLength(totals_scorer) * m_scorer_weights[scorerNum];
+      last += numScoresScorer;
+      scorerNum++;
+    }
+    return refLen;
+  }
+
 void InterpolatedScorer::setReferenceFiles(const vector<string>& referenceFiles)
 {
   for (ScopedVector<Scorer>::iterator itsc = m_scorers.begin();
diff --git a/mert/InterpolatedScorer.h b/mert/InterpolatedScorer.h
index d1078e9e1..159abf6d4 100644
--- a/mert/InterpolatedScorer.h
+++ b/mert/InterpolatedScorer.h
@@ -39,6 +39,10 @@ public:
 
   virtual void setScoreData(ScoreData* data);
 
+  virtual float calculateScore(const std::vector<ScoreStatsType>& totals) const;
+
+  virtual float getReferenceLength(const std::vector<ScoreStatsType>& totals) const;
+
   /**
    * Set the factors, which should be used for this metric
    */
diff --git a/mert/Scorer.h b/mert/Scorer.h
index 0424398eb..8c468aff2 100644
--- a/mert/Scorer.h
+++ b/mert/Scorer.h
@@ -42,6 +42,19 @@ public:
    */
   virtual std::size_t NumberOfScores() const = 0;
 
+  /**
+   * Calculate score based on a vector of sufficient statistics.
+   */
+  virtual float calculateScore(const std::vector<ScoreStatsType>& totals) const = 0;
+
+  float calculateSentenceLevelBackgroundScore(const std::vector<ScoreStatsType>& totals, const std::vector<ScoreStatsType>& bg) {
+    std::vector<ScoreStatsType> stats(totals.size());
+    for(size_t i=0; i<stats.size(); i++)
+      stats[i] = totals[i]+bg[i];
+    // Get score and scale by reference length (as per Chiang et al 08)
+    return calculateScore(stats) * getReferenceLength(stats);
+  }
+
   /**
    * Set the reference files. This must be called before prepareStats().
    */
@@ -97,6 +110,11 @@ public:
     return 0;
   }
 
+  /**
+   * Based on vector of sufficient statistics, return length of reference.
+   */
+  virtual float getReferenceLength(const std::vector<ScoreStatsType>& totals) const = 0;
+
   /**
    * Set the score data, prior to scoring.
    */
diff --git a/mert/StatisticsBasedScorer.h b/mert/StatisticsBasedScorer.h
index 36108d639..f1c77e0ba 100644
--- a/mert/StatisticsBasedScorer.h
+++ b/mert/StatisticsBasedScorer.h
@@ -11,6 +11,8 @@
 
 #include "Scorer.h"
 
+#include "util/exception.hh"
+
 namespace MosesTuning
 {
 
@@ -21,6 +23,8 @@ namespace MosesTuning
  */
 class StatisticsBasedScorer : public Scorer
 {
+friend class HopeFearDecoder;
+
 public:
   StatisticsBasedScorer(const std::string& name, const std::string& config);
   virtual ~StatisticsBasedScorer() {}
@@ -40,6 +44,11 @@ protected:
    */
   virtual statscore_t calculateScore(const std::vector<ScoreStatsType>& totals) const = 0;
 
+  virtual float getReferenceLength(const std::vector<ScoreStatsType>& totals) const {
+    UTIL_THROW(util::Exception, "getReferenceLength not implemented for this scorer type.");
+    return 0;
+  }
+
   // regularisation
   RegularisationType m_regularization_type;
   std::size_t  m_regularization_window;
diff --git a/mert/kbmira.cpp b/mert/kbmira.cpp
index 90f201a00..0be72e9f9 100644
--- a/mert/kbmira.cpp
+++ b/mert/kbmira.cpp
@@ -46,6 +46,9 @@ de recherches du Canada
 #include "MiraFeatureVector.h"
 #include "MiraWeightVector.h"
 
+#include "Scorer.h"
+#include "ScorerFactory.h"
+
 using namespace std;
 using namespace MosesTuning;
 
@@ -57,6 +60,8 @@ int main(int argc, char** argv)
   string denseInitFile;
   string sparseInitFile;
   string type = "nbest";
+  string sctype = "BLEU";
+  string scconfig = "";
   vector<string> scoreFiles;
   vector<string> featureFiles;
   vector<string> referenceFiles; //for hg mira
@@ -78,6 +83,8 @@ int main(int argc, char** argv)
   desc.add_options()
   ("help,h", po::value(&help)->zero_tokens()->default_value(false), "Print this help message and exit")
   ("type,t", po::value<string>(&type), "Either nbest or hypergraph")
+  ("sctype", po::value<string>(&sctype), "the scorer type (default BLEU)")
+  ("scconfig,c", po::value<string>(&scconfig), "configuration string passed to scorer")
   ("scfile,S", po::value<vector<string> >(&scoreFiles), "Scorer data files")
   ("ffile,F", po::value<vector<string> > (&featureFiles), "Feature data files")
   ("hgdir,H", po::value<string> (&hgDir), "Directory containing hypergraphs")
@@ -209,19 +216,20 @@ int main(int argc, char** argv)
 
   MiraWeightVector wv(initParams);
 
-  // Initialize background corpus
-  vector<ValType> bg;
-  for(int j=0; j<kBleuNgramOrder; j++) {
-    bg.push_back(kBleuNgramOrder-j);
-    bg.push_back(kBleuNgramOrder-j);
+  // Initialize scorer
+  if(sctype != "BLEU" && type == "hypergraph") {
+    UTIL_THROW(util::Exception, "hypergraph mira only supports BLEU");
   }
-  bg.push_back(kBleuNgramOrder);
+  boost::scoped_ptr<Scorer> scorer(ScorerFactory::getScorer(sctype, scconfig));
+
+  // Initialize background corpus
+  vector<ValType> bg(scorer->NumberOfScores(), 1);
 
   boost::scoped_ptr<HopeFearDecoder> decoder;
   if (type == "nbest") {
-    decoder.reset(new NbestHopeFearDecoder(featureFiles, scoreFiles, streaming, no_shuffle, safe_hope));
+    decoder.reset(new NbestHopeFearDecoder(featureFiles, scoreFiles, streaming, no_shuffle, safe_hope, scorer.get()));
   } else if (type == "hypergraph") {
-    decoder.reset(new HypergraphHopeFearDecoder(hgDir, referenceFiles, initDenseSize, streaming, no_shuffle, safe_hope, hgPruning, wv));
+    decoder.reset(new HypergraphHopeFearDecoder(hgDir, referenceFiles, initDenseSize, streaming, no_shuffle, safe_hope, hgPruning, wv, scorer.get()));
   } else {
     UTIL_THROW(util::Exception, "Unknown batch mira type: '" << type << "'");
   }
diff --git a/scripts/training/mert-moses.pl b/scripts/training/mert-moses.pl
index 21ae0b5e4..09f2b4912 100755
--- a/scripts/training/mert-moses.pl
+++ b/scripts/training/mert-moses.pl
@@ -386,7 +386,14 @@ if ($__PROMIX_TRAINING) {
   die "To use promix training, need to specify a filter and binarisation command" unless   $filtercmd =~ /Binarizer/;
 }
 
-$mertargs = "" if !defined $mertargs;
+if (!defined $mertargs) {
+  if (defined $batch_mira_args) {
+    $mertargs = $batch_mira_args;
+  }
+  else {
+    $mertargs = "";
+  }
+}
 
 my $scconfig = undef;
 if ($mertargs =~ /\-\-scconfig\s+(.+?)(\s|$)/) {