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Multi-Task Minimum Error Rate Training

This set of scripts is essentially a wrapper around the script which
is distributed with moses ( In its current implementation
this is used for running several MERT runs in parallel and altering resulting weights
after each run. It also manages convergence.

With MMERT we transfer the basic idea of regularized multi-task learning to MERT. That is,
in each iteration we run a separate instance of MERT for each task, and then regularize
the returned weight vectors towards the average weight vector of the previous iteration
by adding or subtracting the regularization parameter lambda. As simply adding or subtracting
lambda is only an approximation of regularization, we clip the new value at the average,
if the average would be crossed otherwise.

See the "Multi-Task Minimum Error Rate Training for SMT" (Simianer et al. 2011) paper for further details.

This was tested with r4106 of moses trunk.
NOTE: v0.1 worked only upto r4065, because script produced incompatible *.init.opt files.

An exemplary experiment installation can be found in the example/ directory (4 models,
built from the first 8 sentences of europarl v6 de-en,

    3 binaries from a moses build: extractor, mert and moses
    Put target/source language data (development sets) in here.
    Use a common file naming scheme (this has to be defined in
    One or several configuration files (ini) for use with moses, these can be defined per 'task' or 'one-fits-all'
    This is useful if you want to start MERT with different initial weights for each run
    or different model files.
    3 scripts:, and
    In default configuration work directories are created here.
    Language model, phrase table and reordering model files. Path has to be defined
    in the ini(s) in ini/.
    The scripts distributed with moses (needed by mert,

Note: The phrase and reordering table(s) should be filtered against
each dev set, see to fit into memory.
The language model can be read from disk using kenlm models (the one distributed
in this tarball is kenlm v4).

After putting moses binaries into bin/ and moses scripts in scripts/ the experiment can be run with
 $ cd ~/mmert/mmert/;./ SUFFIX
Assuming you extracted mmert in your home directory and adjusted the username in
the paths in the ini/moses.ini file(!).
SUFFIX is used for creating working directories.

After convergence (max change in the average vector is lower as the MIN_CHANGE parameter),
you can find the final weight vectors in the following files:
 * Average: mmert_SUFFIX/runX.avector.txt (X is the last iteration)
 * Individual: mert_SUFFIX_TASK/runX.mert.log in the line 'Best point:'
The weights in the example are ordered as follows:
  d d d d d d d lm w tm tm tm tm tm
This can be different in your installation, if you use more or less models.
The order can be found in the stdout output of MERT (e.g. mmert_SUFFIX/mert.TASK.out).
$FIRST_AVG: 'first average' to clip against
  0: 0-vector
  1: average of start weights (put the vector as a file name run0.avector.txt into mmert_SUFFIX)
  Define to use several or one moses configuration files.
  This can be used for using several model files, using different initial weights etc.
  Regularization parameter, useful values: 0.1 .. 0.05 .. 0.0000001
  Stopping criterion: minimum change in average vector. Useful values: 0.01 .. 0.00001.
  Everything above above ~0.2 leads to convergence after 1 iteration because
  MERT normalizes the weight vector.
More parameters are documented in itself.
Does actual regularization/clipping. Reads and writes current mert.log (the mert binary reads/writes
weights in there).
 * the outer 'while 1' loop (line 613 to 810) was commented out 
 * normalization was fixed (division by 0 if we start 0 weights in a moses.ini)

Version History
0.1 initial release
0.2 make code better readable, updated script to current trunk r4106


Multi-task learning for Moses' minimum error rate training, probably deprecated







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