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authorPatrick Simianer <p@simianer.de>2011-11-13 22:12:46 +0100
committerPatrick Simianer <p@simianer.de>2011-11-13 22:12:46 +0100
commitf99ba621e5bd10b069b453d11b3b4981dc482b6c (patch)
treece35e2ce10e81c2b196c89bf8a0f48f7bbdc7683 /dtrain/README.md
parent015bc0182017488e4917ae11e54fa0cf3533f3bc (diff)
new reducer: active on all tasks
Diffstat (limited to 'dtrain/README.md')
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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]