Moses-support Digest, Vol 125, Issue 25

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Today's Topics:

1. Please help me with FACTORED MODEL ~~! (Tran Anh)


----------------------------------------------------------------------

Message: 1
Date: Mon, 13 Mar 2017 22:44:06 +0800
From: Tran Anh <anhuni1006@gmail.com>
Subject: [Moses-support] Please help me with FACTORED MODEL ~~!
To: moses-support@mit.edu
Message-ID:
<CA+QuFCQob2xUdqJyRTd4=JWhQUfkyRsHgzYCHez0kXy5VyDOkw@mail.gmail.com>
Content-Type: text/plain; charset="utf-8"

Dear all,

I have done experiments with Factored model. The tuning and testing is done
with source text annotated with the same factors as during the training.
The target text is clean, without factors.

I found that my factored model (bleu score = 22.2) higher than bleu score
of Baseline = 21.11(no factor).
Training command has translation factors and generation factors steps:
(......--translation-factors 0-0+1-1+2-2 --generation-factors 2,3-0.....).

*This is moses.ini file (trainig is finished, but notyet tuning):*

#########################
### MOSES CONFIG FILE ###
#########################

# input factors
[input-factors]
0
1
2

# mapping steps
[mapping]
0 T 0
0 T 1
0 T 2

[distortion-limit]
6

# feature functions
[feature]
UnknownWordPenalty
WordPenalty
PhrasePenalty
PhraseDictionaryMemory name=TranslationModel0 num-features=4
path=/home/yychen/55factor-hz4new-VC/train2-ge3/train/model/phrase-table.0-0.gz
input-factor=0 output-factor=0
PhraseDictionaryMemory name=TranslationModel1 num-features=4
path=/home/yychen/55factor-hz4new-VC/train2-ge3/train/model/phrase-table.1-1.gz
input-factor=1 output-factor=1
PhraseDictionaryMemory name=TranslationModel2 num-features=4
path=/home/yychen/55factor-hz4new-VC/train2-ge3/train/model/phrase-table.2-2.gz
input-factor=2 output-factor=2
Generation name=GenerationModel0 num-features=2
path=/home/yychen/55factor-hz4new-VC/train2-ge3/train/model/generation.2,3-0.gz
input-factor=2,3 output-factor=0
LexicalReordering name=LexicalReordering0 num-features=6
type=wbe-msd-bidirectional-fe-allff input-factor=0 output-factor=0
path=/home/yychen/55factor-hz4new-VC/train2-ge3/train/model/
reordering-table.0-0.wbe-msd-bidirectional-fe.gz
Distortion
KENLM lazyken=0 name=LM0 factor=0 path=/home/yychen/55factor-hz4
new-VC/train2-ge3/vi-ch.lm.ch order=3

# dense weights for feature functions
[weight]
# The default weights are NOT optimized for translation quality. You MUST
tune the weights.
# Documentation for tuning is here: http://www.statmt.org/
moses/?n=FactoredTraining.Tuning
UnknownWordPenalty0= 1
WordPenalty0= -1
PhrasePenalty0= 0.2
TranslationModel0= 0.2 0.2 0.2 0.2
TranslationModel1= 0.2 0.2 0.2 0.2
TranslationModel2= 0.2 0.2 0.2 0.2
GenerationModel0= 0.3 0
LexicalReordering0= 0.3 0.3 0.3 0.3 0.3 0.3
Distortion0= 0.3
LM0= 0.5


*This is moses.ini file (tuning is finished):*

# MERT optimized configuration
# decoder /opt/moses/bin/moses
# BLEU 0.200847 on dev /home/yychen/55factor-hz4new-VC/tun2-ge3/vi.tun4-new
# We were before running iteration 4
# finished 2017? 01? 08? ??? 19:51:49 CST
### MOSES CONFIG FILE ###
#########################

# input factors
[input-factors]
0
1
2

# mapping steps
[mapping]
0 T 0



#[decoding-graph-backoff]
#0
#1

[distortion-limit]
6

# feature functions
[feature]
UnknownWordPenalty
WordPenalty
PhrasePenalty
PhraseDictionaryMemory name=TranslationModel0 num-features=4
path=/home/yychen/55factor-hz4new-VC/train2-ge3/train/model/phrase-table.0-0.gz
input-factor=0 output-factor=0
PhraseDictionaryMemory name=TranslationModel1 num-features=4
path=/home/yychen/55factor-hz4new-VC/train2-ge3/train/model/phrase-table.1-1.gz
input-factor=1 output-factor=1
PhraseDictionaryMemory name=TranslationModel2 num-features=4
path=/home/yychen/55factor-hz4new-VC/train2-ge3/train/model/phrase-table.2-2.gz
input-factor=2 output-factor=2
Generation name=GenerationModel0 num-features=2
path=/home/yychen/55factor-hz4new-VC/train2-ge3/train/model/generation.2,3-0.gz
input-factor=2,3 output-factor=0
LexicalReordering name=LexicalReordering0 num-features=6
type=wbe-msd-bidirectional-fe-allff input-factor=0 output-factor=0
path=/home/yychen/55factor-hz4new-VC/train2-ge3/train/model/
reordering-table.0-0.wbe-msd-bidirectional-fe.gz
Distortion
KENLM lazyken=0 name=LM0 factor=0 path=/home/yychen/55factor-hz4
new-VC/train2-ge3/vi-ch.lm.ch order=3

# dense weights for feature functions
[weight]

LexicalReordering0= 0.0421305 0.0145905 0.0421305 0.0419472 0.0571605
0.110762
Distortion0= 0.0357908
LM0= 0.0702177
WordPenalty0= -0.140435
PhrasePenalty0= 0.037449
TranslationModel0= 0.00820789 0.0280871 0.117941 -0.00550954
TranslationModel1= 0.0280871 0.0273782 -0.0150248 0.0280871
TranslationModel2= 0.0453928 0.00576192 0.0280871 0.0276907
GenerationModel0= 0.0421305 0
UnknownWordPenalty0= 1

Here, I want to try translate " ? ? ? " in source language to target
language by using n-best.
However, I want to demonstrate why that result is better BASELINE, by using
n-best (% moses -f moses.ini -n-best-list listfile2 < in).
When tuning process is finished, i tried to translate some resource
sentences to target sentences. But, parameters of TranslationModel0 ( map
0-0) is changed, while the parameters of (TranslationModel1,
TranslationModel2, GenerationModel0) are 0 0 0 0. Translation results is as
follows . *(here, n = 2)**:*



0 ||| ? ? ? ||| LexicalReordering0= -1.60944 0 0 0 0 0 Distortion0= 0 LM0=
-15.2278 LM1= -699.809 WordPenalty0= -3 PhrasePenalty0= 1
TranslationModel0= -1.38629 -2.20651 0 -2.21554 *TranslationModel1= 0 0 0 0
TranslationModel2= 0 0 0 0 GenerationModel0= 0 0* ||| -0.589076
0 ||| ? ? ? ||| LexicalReordering0= -1.86048 0 0 -0.510826 0 0
Distortion0= 0 LM0= -15.2278 LM1= -699.809 WordPenalty0= -3
PhrasePenalty0= 2 TranslationModel0= -2.86909 -2.20651 -0.09912
-2.21554 *TranslationModel1=
0 0 0 0 TranslationModel2= 0 0 0 0 GenerationModel0= 0 0* ||| -0.727864

I want to compare my factored model with baseline at every translation step
in SMT to explain why my model is good.
So I want to ask you:

1. Can you explain for me why that parameters are 0 0 0 0.?
2. My factors which I added to factored model are useful or not?
3. How to get the parameters in translation result (n-best) of
*TranslationModel1, **TranslationModel2, **GenerationModel0* is different
to 0 0 0 0?

i am waiting for you reply ~~!
Thank you so much!
With best regards,

Tran Anh,
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