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| -rw-r--r-- | dtrain/README.md | 34 |
1 files changed, 12 insertions, 22 deletions
diff --git a/dtrain/README.md b/dtrain/README.md index 71641bd8..d6699cb4 100644 --- a/dtrain/README.md +++ b/dtrain/README.md @@ -3,34 +3,24 @@ dtrain 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 +* *MULTIPARTITE* ranking (1 vs all, cluster model/score) +* *REMEMBER* sampled translations (merge) +* *SELECT* iteration with highest (_real_) BLEU? +* *GENERATED* data? (perfect translation in kbest) +* *CACHING* (ngrams for scoring) +* hadoop *PIPES* imlementation +* *ITERATION* variants (shuffle resulting weights, re-iterate) +* *MORE THAN ONE* reference for BLEU? +* *RANDOM RESTARTS* +* use separate TEST SET for each shard Uncertain, known bugs, problems ------------------------------- -* cdec kbest vs 1best (no -k param), rescoring? => ok(?) +* cdec kbest vs 1best (no -k param), rescoring (ref?)? => 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 +* input/grammar caching (strings, files) FIXME ----- |