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Carmel finite-state toolkit - J. Graehl

(carmel includes EM and gibbs-sampled (pseudo-Bayesian) training)

(see carmel/LICENSE - free for research/non-commercial)

(see carmel/README and carmel/carmel-tutorial).

Building from source

mkdir build
cd build
cmake ..
make
make install

optional cmake parameters:

  • -DCMAKE_INSTALL_PREFIX=/custom/install/path
  • -DBOOST_ROOT=/path/to/boost (if it is not installed in standard location)
  • -DOPENFST_ROOT=/path/to/openfst (if desired, and it is not installed in standard location)

prerequisites:

  • cmake 3.1 or higher
  • a C++11 compiler
  • Boost. Tested on versions between 1.53 and 1.59, but others should work, too
  • Optionally, openfst (http://www.openfst.org)

Subdirectories

  • carmel: finite state transducer toolkit with EM and gibbs-sampled (pseudo-Bayesian) training

  • forest-em: derivation forests EM and gibbs (dirichlet prior bayesian) training

  • graehl/shared: utility C++/Make libraries used by carmel and forest-em

  • gextract: some python bayesian syntax MT rule inference

  • sblm: some simple pcfg (e.g. penn treebank parses, but preferably binarized)

  • clm: some class-based LM feature? I forget.

  • cipher: some word-class discovery and unsupervised decoding of simple probabilistic substitution cipher (uses carmel, but look to the tutorial in carmel/ first)

  • util: misc shell/perl scripts

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finite-state toolkit, EM and Bayesian (Gibbs sampling) training for FST and context-free derivation forests

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