From 2cdc7dd166a91e2ca1fa8aeb0a0836eb2c80cf5c Mon Sep 17 00:00:00 2001 From: Patrick Simianer Date: Wed, 8 Jan 2014 18:33:06 +0100 Subject: version 0.2 --- README | 97 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 97 insertions(+) create mode 100644 README (limited to 'README') diff --git a/README b/README new file mode 100644 index 0000000..ce08232 --- /dev/null +++ b/README @@ -0,0 +1,97 @@ +Multi-Task Minimum Error Rate Training +====================================== + +This set of scripts is essentially a wrapper around the mert-moses.pl script which +is distributed with moses (http://statmt.org/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 mert-moses.pl script produced incompatible *.init.opt files. + +Usage +===== +An exemplary experiment installation can be found in the example/ directory (4 models, +built from the first 8 sentences of europarl v6 de-en, http://statmt.org/europarl/): + +example/ + bin/ + 3 binaries from a moses build: extractor, mert and moses + data/ + Put target/source language data (development sets) in here. + Use a common file naming scheme (this has to be defined in inbetwmert.sh). + ini/ + 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. + mmert/ + 3 scripts: inbetwmert.sh, regmtl.py and mert-moses.pl + In default configuration work directories are created here. + models/ + Language model, phrase table and reordering model files. Path has to be defined + in the ini(s) in ini/. + scripts/ + The scripts distributed with moses (needed by mert, http://www.statmt.org/moses/?n=Moses.SupportTools). + +Note: The phrase and reordering table(s) should be filtered against +each dev set, see http://www.statmt.org/moses/?n=Moses.SupportTools#ntoc3 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/;./inbetwmert.sh 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). + +inbetwmert.sh +------------- +$FIRST_AVG: 'first average' to clip against + 0: 0-vector + 1: average of start weights (put the vector as a file name run0.avector.txt into mmert_SUFFIX) +$INI or $INIS + Define to use several or one moses configuration files. + This can be used for using several model files, using different initial weights etc. +$LAMBDA + Regularization parameter, useful values: 0.1 .. 0.05 .. 0.0000001 +$MIN_CHANGE + 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 inbetwmert.sh itself. + +regmtl.py +--------- +Does actual regularization/clipping. Reads and writes current mert.log (the mert binary reads/writes +weights in there). + +mert-moses.pl +------------- +Changes/hacks: + * 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 mert-moses.pl script to current trunk r4106 + -- cgit v1.2.3