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-TODO
- 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
-
-KNOWN BUGS, PROBLEMS
- doesn't select best iteration for weigts
- if size of candidate < N => 0 score
- cdec kbest vs 1best (no -k param), rescoring? => ok(?)
- no sparse vector in decoder => ok
- ? ok
- sh: error while loading shared libraries: libreadline.so.6: cannot open shared object file: Error 24
- PhraseModel_* features (0..99 seem to be generated, why 99?)
- flex scanner jams on malicious input, we could skip that
-
-FIX
-  merge
-  ep data