README

1 files changed, 72 insertions, 52 deletions

diff --git a/dtrain/README.md b/dtrain/README.md
index 3d09393c..69df83a5 100644
--- a/dtrain/README.md
+++ b/dtrain/README.md
@@ -6,49 +6,52 @@ Build & run
 build ..
 <pre>
 git clone git://github.com/qlt/cdec-dtrain.git
-cd cdec_dtrain
-autoreconf -ifv
-./configure
+cd cdec-dtrain
+autoreconf -if[v]
+./configure [--disable-gtest]
 make
 </pre>
 and run:
 <pre>
 cd dtrain/hstreaming/
 (edit ini files)
-edit hadoop-streaming-job.sh $IN and $OUT
+edit the vars in hadoop-streaming-job.sh ($ID, $IN and $OUT)
 ./hadoop-streaming-job.sh
 </pre>
 
-
 Ideas
 -----
-* *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)
+* *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*
-* use separate TEST SET for each shard
+* *RANDOM RESTARTS* or directions
+* use separate *TEST SET* for each shard
+* *REDUCE* training set (50k?)
+* *SYNTAX* features (CD)
 
 Uncertain, known bugs, problems
 -------------------------------
 * 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)
+* 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)
 
-FIXME
------
-merge dtrain part-* files
-mapred count shard sents
-250 forest sampling is real bad, bug?
-kenlm not portable (i7-2620M vs Intel(R) Xeon(R) CPU E5620 @ 2.40GHz)
-metric reporter of bleu for each shard
-mapred chaining? hamake?
+FIXME, todo
+-----------
+* merge dtrain part-X files, for better blocks
+* mapred count shard sents
+* 250 forest sampling is real bad, bug?
+* metric reporter of bleu for each shard
+* kenlm not portable (i7-2620M vs Intel(R) Xeon(R) CPU E5620 @ 2.40GHz)
+* mapred chaining? hamake?
 
 Data
 ----
@@ -63,57 +66,74 @@ nc-v6.de-en.cs.loo      peg
 ep-v6.de-en.cs          pe
 ep-v6.de-en.cs.loo      p
 
-p: prep, e: extract, g: grammar, d: dtrain
+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
-
+[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
+ --symgiza as of Oct 1 2011--
  ---
- randomly sample 100 from train.loo
- run mira/dtrain for 50/60 iterations
- w/o lm, wp
- measure ibm_bleu on exact same sents
+ 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
+
+[1]
+lm?
+ 3-4-5
+ open
+ unk
+ nounk (-100 for unk)
+ --
+ lm oov weight pos?
+ no tuning, -100 prob for unk EXPECT: nounk
+ tuning with dtrain EXPECT: open
 
+[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]
 pro metaparam
  (max) iter
  regularization
+ ???
  
 mira metaparam
  (max) iter: 10 (nc???) vs 15 (ep???)
 
-lm?
- 3-4-5
- open
- unk
- nounk (-100 for unk)
- --
- tune or not???
- lm oov weight pos?
-
 features to try
  NgramFeatures -> target side ngrams
  RuleIdentityFeatures