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+IDEAS
+=====
+ MULTIPARTITE ranking (108010, 1 vs all, cluster modelscore;score)
+ what about RESCORING?
+ REMEMBER kbest (merge) weights?
+ SELECT iteration with highest (real) BLEU?
+ GENERATED data? (multi-task, ability to learn, perfect translation in nbest, at first all modelscore 1)
+ CACHING (ngrams for scoring)
+ hadoop PIPES imlementation
+ SHARED LM (kenlm actually does this!)?
+ ITERATION variants
+  once -> average
+  shuffle resulting weights
+ weights AVERAGING in reducer (global Ngram counts)
+ BATCH implementation (no update after each Kbest list)
+ set REFERENCE for cdec (rescoring)?
+ MORE THAN ONE reference for BLEU?
+ kbest NICER (do not iterate twice)!? -> shared_ptr?
+ DO NOT USE Decoder::Decode (input caching as WordID)!?
+  sparse vector instead of vector<double> for weights in Decoder(::SetWeights)?
+ reactivate DTEST and tests
+ non deterministic, high variance, RANDOM RESTARTS
+ use separate TEST SET
+
+Uncertain, known bugs, problems
+===============================
+* cdec kbest vs 1best (no -k param), rescoring? => ok(?)
+* no sparse vector in decoder => ok/fixed
+* PhraseModel_* features (0..99 seem to be generated, why 99?)
+* flex scanner jams on malicious input, we could skip that
+
+FIXME
+=====
+* merge
+* ep data
+
+Data
+====
+<pre>
+nc-v6.de-en             peg
+nc-v6.de-en.loo         peg
+nc-v6.de-en.giza.loo    peg
+nc-v6.de-en.symgiza.loo pe
+nv-v6.de-en.cs          pe
+nc-v6.de-en.cs.loo      pe
+--
+ep-v6.de-en.cs          p
+ep-v6.de-en.cs.loo      p
+</pre>
+