Hi,
I added a new score producer that produces one score, and a
corresponding parameter called weight-r, to my moses binary. I made a
unit test, and I get correct scoring and translation behavior if I run
my test with these two excerpted versions of the ini file:
[weight-r]
0
or
[weight-r]
-99
So I have implemented my feature correctly in the moses binary. Now I
am trying to learn its weight during MERT training. I am encountering
a problem that the new feature weight is not updated after each
iteration. Each one of the runN.moses.ini that are generated during
mert training have
[weight-r]
-1
which is the initial value. Here is how I changed mert-moses.pl to try
to make it also train weight-r:
"lm" => [ [ 1.0, 0.0, 2.0 ] ], # language model
"g" => [ [ 1.0, 0.0, 2.0 ], # generation model
[ 1.0, 0.0, 2.0 ] ],
+ "r" => [ [ 0.0, -1.0, 1.0 ] ], # rift penalty
"tm" => [ [ 0.3, 0.0, 0.5 ], # translation model
[ 0.2, 0.0, 0.5 ],
...
-my $ABBR_FULL_MAP = "d=weight-d lm=weight-l tm=weight-t w=weight-w
g=weight-generation";
+my $ABBR_FULL_MAP = "d=weight-d lm=weight-l tm=weight-t w=weight-w
r=weight-r g=weight-generation";
Am I some other change I need to make to mert-moses.pl so that it
learns a weight for r? To help me debug this, could somebody help me
understand these points?
* What are the values stored in runN.feats.opt.gz? (Why are there many
columns, more than the number of weights? Why are the last columns all
whole numbers?)
* Is there documentation regarding what runN.cands.opt and runN.init.opt are?
By the way, the is for a rift-words feature function, which is 1 for
each phrase case that crosses a comma, quote mark, or some other
similar token. Redecoding with this feature weighted strongly
negatively gave me BLEU improvements in half of my experiments, and I
hope that automatically learning its best weight might give an
improvement across the board, or at least justify leaving this feature
weighted strongly negatively even if BLEU score drops.
Thank you very very much!
--Jason
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