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dtrain
======
Build & run
-----------
build ..
<pre>
git clone git://github.com/qlt/cdec-dtrain.git
cd cdec-dtrain
autoreconf -if[v]
./configure [--disable-gtest]
make
</pre>
and run:
<pre>
cd dtrain/hstreaming/
(edit ini files)
edit the vars in hadoop-streaming-job.sh ($ID, $IN and $OUT)
./hadoop-streaming-job.sh
</pre>
Ideas
-----
* *MULTIPARTITE* ranking (1 vs rest, cluster model/score)
* *REMEMBER* sampled translations (merge kbest lists)
* *SELECT* iteration with highest real BLEU on devtest?
* *GENERATED* data? (perfect translation always in kbest)
* *CACHE* ngrams for scoring
* hadoop *PIPES* imlementation
* *ITERATION* variants (shuffle resulting weights, re-iterate)
* *MORE THAN ONE* reference for BLEU?
* *RANDOM RESTARTS* or directions
* use separate *TEST SET* for each shard
* *REDUCE* training set (50k?)
* *SYNTAX* features (CD)
* distribute *DEV* set to all nodes, avg
Uncertain, known bugs, problems
-------------------------------
* cdec kbest vs 1best (no -k param), rescoring (ref?)? => ok(?)
* no sparse vector in decoder => ok/fixed
* PhraseModel features, mapping?
* flex scanner jams on bad input, we could skip that
* input/grammar caching (strings -> WordIDs)
* look at forest sampling...
* 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?
random notes
------------
* learning rate tuned with perceptron
* aer correlation with bleu?
* dtrain (perc) used for some tests because no optimizer instability
* http://www.ark.cs.cmu.edu/cdyer/dtrain/
* repeat as often as max needed by any learner!
* don't compare lms with diff vocab (stupid backoff paper)
* what does mira/pro optimize?
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)
* 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.
FIXME, todo
-----------
* merge dtrain part-X files, for better blocks (how to do this with 4.5tb ep)
* mapred count shard sents
* mapred stats for learning curve (output weights per iter for eval on devtest)
* 250 forest sampling is real bad, bug?
* metric reporter of bleu for each shard (reporters, status?)
to draw learning curves for all shards in 1 plot
* kenlm not portable (i7-2620M vs Intel(R) Xeon(R) CPU E5620 @ 2.40GHz)
* mapred chaining? hamake?
* make our sigtest work with cdec
* l1l2 red (tsuroke)?
* epsilon stopping criterion
* normalize weight vector to get proper model scores for forest sampling
* 108010 with gap(s), and/or fix (same score in diff groups)
* 108010: combine model score + bleu
* visualize weight vector
* *100 runs stats
* correlation of *_bleu to ibm_bleu
* ep: open lm, cutoff @1
* tune regs
* 3x3 4x4 5x5 .. 10x10 until standard dev ok
* avg weight vector for dtrain? (mira non-avg)
* repeat lm choose with mira/pro
* shuffle training data
Data
----
<pre>
nc-v6.de-en apegd
nc-v6.de-en.loo apegd
nc-v6.de-en.giza apegd
nc-v6.de-en.giza.loo apegd
nc-v6.de-en.cs.giza apegd
nc-v6.de-en.cs.giza.loo apegd
nv-v6.de-en.cs apegd
nc-v6.de-en.cs.loo apegd
--
ep-v6.de-en.cs apegd
ep-v6.de-en.cs.loo apegd
a: alignment:, p: prep, e: extract,
g: grammar, d: dtrain
</pre>
Experiments
-----------
[grammar stats
oov on dev/devtest/test
size
#rules (uniq)
time for building
ep: 1.5 days on 278 slots (30 nodes)
nc: ~2 hours ^^^
lm stats
oov on dev/devtest/test
perplex on train/dev/devtest/test?]
[0]
which word alignment?
berkeleyaligner
giza++ as of Sep 24 2011, mgizapp 0.6.3
--symgiza as of Oct 1 2011--
---
NON LOO
(symgiza unreliable)
randomly sample 100 from train with loo
run dtrain for 100 iterations
w/o all other feats (lm, wp, ...) +Glue
measure ibm bleu on exact same sents
ep -> berkeleyaligner ??? (mb per sent, rules per sent)
[1]
lm?
3-4-5
open
unk
nounk (-100 for unk)
--
lm oov weight pos? -100
no tuning, -100 prob for unk EXPECT: nounk
tuning with dtrain EXPECT: open
=>
lmtest on cs.giza.loo???
[2]
cs?
'default' weights
[3]
loo vs non-loo
'jackknifing'
generalization (determ.!) on dev, test on devtest
[4]
stability
all with default params
mira: 100
pro: 100
vest: 100
dtrain: 100
[undecided]
do we even need loo for ep?
pro metaparam
(max) iter
regularization
???
mira metaparam
(max) iter: 10 (nc???) vs 15 (ep???)
features to try
NgramFeatures -> target side ngrams
RuleIdentityFeatures
RuleNgramFeatures -> source side ngrams from rule
RuleShape -> relative orientation of X's and terminals
SpanFeatures -> http://www.cs.cmu.edu/~cdyer/wmt11-sysdesc.pdf
ArityPenalty -> Arity=0 Arity=1 and Arity=2
---
variables to control
[alignment]
[lm]
[vest]
[mira]
[dtrain]
[pro]
--------
In PRO, a continually growing list of candidates is maintained for
each sentence by concatenating k-best lists from each decoding run,
and the training pairs are sampled from them. This is done to ensure
that the optimizer doesn't forget about bad places in the parameter
space that it visited previously (since some training samples will be
selected from that space). Something like your approach should work
well though, provided you don't overfit to the sentence pair you're
looking at in each iteration. So I guess the question is: what are you
doing in step 2 exactly? A complete optimization? Taking one step? The
other thing is, do you maintain n-best hypotheses from previous
iterations?
--------
good grammar? => ability to overfit
berkeley vs giza
not LOO
NO optimizer instability
20+ iterations
approx_bleu-4
train on dev => test on dev
train on devtest => test on devtest
dev on dev better?
devtest on devtest better?
(train/test on loo? => lower!)
(test on others => real bad)
loo vs non-loo? => generalization
(cs vs non-cs?)
giza||berkeley
LOO + non LOO
2 fold cross validation
train on dev, test on devtest
train on devtest, test on dev
as above ^^^
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