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Diffstat (limited to 'dtrain/README.md')
| -rw-r--r-- | dtrain/README.md | 38 |
1 files changed, 33 insertions, 5 deletions
diff --git a/dtrain/README.md b/dtrain/README.md index faedf8a7..46f783b0 100644 --- a/dtrain/README.md +++ b/dtrain/README.md @@ -34,6 +34,7 @@ Ideas * *REDUCE* training set (50k?) * *SYNTAX* features (CD) * distribute *DEV* set to all nodes, avg +* *PARAPHRASES* for better approx BLEU? Uncertain, known bugs, problems @@ -47,10 +48,11 @@ Uncertain, known bugs, problems * devtest loo or not? why loo grammars larger? (sort psgs | uniq -> grammar) * lower beam size to be faster? * why is <unk> -100 in lm so good? -* noise helps? +* noise helps for discriminative training? * what does srilm do with -unk but nothing mapped to unk (<unk> unigram)? => this: http://www-speech.sri.com/pipermail/srilm-user/2007q4/000543.html -* mira translation sampling? +* mira translation sampling? => done +* does AER correlate with BLEU? random notes ------------ @@ -61,16 +63,25 @@ random notes * repeat as often as max needed by any learner! * don't compare lms with diff vocab (stupid backoff paper) * what does mira/pro optimize? +* early stopping +* 10-20k rules per sent normal +* shard size 500 -> 2k +* giza vs. berkeleyaligner: giza less noise? +* compound splitting -> more rules? +* loo => ref can't be reached? (jackknifing) +* prune singletons -> less noise? (do I do this?) +* random sample: take 100 at random features -------- * baseline features (take whatever cdec implements for VEST) * rule identifiers (feature name = rule as string) -* rule discounts (taken from frequency i or frequency interval [i,j] of rule in extraction from parallel training data) -* target ngrams (from nonterminals in rule rhs) +* rule discounts (taken from frequency i or frequency interval [i,j] of rule in extraction from parallel training data) bins +* target ngrams (from nonterminals in rule rhs), with gaps? * source-target unigrams (from word alignments used in rule extraction, if they are?) * lhs, rhs, rule length features * all other features depend on syntax annotation. +* word alignment FIXME, todo ----------- @@ -93,10 +104,15 @@ FIXME, todo * correlation of *_bleu to ibm_bleu * ep: open lm, cutoff @1 * tune regs -* 3x3 4x4 5x5 .. 10x10 until standard dev ok +* 3x3 4x4 5x5 .. 10x10 until standard dev ok, moving avg * avg weight vector for dtrain? (mira non-avg) * repeat lm choose with mira/pro * shuffle training data +* learning rate dynamic (Duh? Tsuroka?) +* divide updates by ? +* mira: 5/10/15, pro: (5)/10/20/30 (on devtest!) +* sample pairs like in pro +* mira forest sampling Data @@ -146,6 +162,8 @@ which word alignment? measure ibm bleu on exact same sents ep -> berkeleyaligner ??? (mb per sent, rules per sent) +*100 -> triples, quadruples + [1] lm? 3-4-5 @@ -195,6 +213,16 @@ features to try ArityPenalty -> Arity=0 Arity=1 and Arity=2 --- +shard size: 500-2k +iterations, re-iterate (shuffle w): 10 +gamma, eta +SVM, perceptron +reducer: avg (feats/shard), l1l2, active on all shards +sentence sampling: forest +pair sampling: all, rand, 108010 (sort), PRO +out of domain test? + +--- variables to control [alignment] |