moses-smt/mosesdecoder
1
1
Fork 0
mirror of https://github.com/moses-smt/mosesdecoder.git synced 2024-12-30 15:34:01 +03:00
Code Issues Packages Projects Releases Wiki Activity
71295e2708
mosesdecoder/contrib/promix
History
Barry Haddow 71295e2708 correct markdown
2013-04-19 08:45:03 +01:00
..
test_data Create small binarised models for testing 2013-04-18 07:51:06 +01:00
bleu.py Initial checkin. Missing some test data 2013-02-21 17:59:53 +00:00
coll.py Initial checkin. Missing some test data 2013-02-21 17:59:53 +00:00
main.py remove restriction to 2 tables 2013-03-22 10:35:19 +00:00
nbest.py Load all options. Use relative path. 2013-04-17 21:34:20 +01:00
README.md correct markdown 2013-04-19 08:45:03 +01:00
sampler.py Initial checkin. Missing some test data 2013-02-21 17:59:53 +00:00
test_bleu.py Initial checkin. Missing some test data 2013-02-21 17:59:53 +00:00
test_main.py remove absolute path default 2013-04-17 21:35:32 +01:00
test_nbest.py Create small binarised models for testing 2013-04-18 07:51:06 +01:00
test_sampler.py Initial checkin. Missing some test data 2013-02-21 17:59:53 +00:00
test_train.py Initial checkin. Missing some test data 2013-02-21 17:59:53 +00:00
test.py Initial checkin. Missing some test data 2013-02-21 17:59:53 +00:00
train.py Remove debug, update python interface 2013-04-12 18:00:22 +01:00
util.py Initial checkin. Missing some test data 2013-02-21 17:59:53 +00:00

README.md

promix - for training translation model interpolation weights using PRO

Author: Barry Haddow <bhaddow [AT] inf.ed.ac.uk>

ABOUT

The code here provides the "inner loop" for a batch tuning algorithm (like MERT) which optimises phrase table interpolation weights at the same time as the standard linear model weights. Interpolation of the phrase tables uses the "naive" method of tmcombine.

Currently the interpolation only works for two phrase tables, but will soon be extended to work for more than two.

REQUIREMENTS

The scripts require the Moses Python interface (in contrib/python). It should be built first, following the instructions in that directory.

The scripts also require scipy and numpy. They have been tested with the following versions:

  • Python 2.7
  • Scipy 0.11.0
  • Numpy 1.6.2

Run the test.py script to check that everything is functioning correctly.

USAGE

Since the code in this directory provides the inner loop for a batch tuning algorithm, it is run from the increasingly inaccurately named mert-moses.pl. If you want to run the optimiser directly, run main.py -h for usage.

A sample command for mert-moses.pl is as follows: MOSES/scripts/training/mert-moses.pl
input-file ref-file
decoder
ini-file
--promix-training MOSES/contrib/promix/main.py
--maximum-iterations 15
--promix-table phrase-table-1
--promix-table phrase-table-2
--filtercmd "MOSES/scripts/training/filter-model-given-input.pl --Binarizer MOSES/bin/processPhraseTable"
--nbest 100 --working-dir ./tmp --decoder-flags "-threads 4 -v 0 "
--rootdir MOSES/scripts -mertdir MOSES/bin
--return-best-dev

Note that promix training requires a filter and binarise script, and that the phrase table referenced in the ini file is not used. The argument --return-best-dev is not essential, but recommended.

REFERENCES

The code here was created for:

Haddow, Barry (2013) Applying Pairwise Ranked Optimisation to Improve the Interpolation of Translation Models. In: Proceedings of NAACL 2013

See also:

Sennrich, Rico (2012). Perplexity Minimization for Translation Model Domain Adaptation in Statistical Machine Translation. In: Proceedings of EACL 2012.