1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
|
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)
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
* 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?
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
[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]
|
