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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
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