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OxDP

Generative dependency parsing models.

Probability models are based on the oxlm PYP and neural (LBL) language models.

Dependencies

Cmake is needed to build the toolkit. The external dependencies are Boost and OpenMP. Cmake looks for Boost and OpenMP in the locations where the libraries are installed by default using the operating system's package management tool.

Installation

Run the following to compile the code for the first time:

cd oxdp
mkdir build
cd build
cmake ../src
make

Prepare the training and test data

The training and test data uses the CoNLL dependency parsing format. The script

oxdp/scripts/preprocess-conll.py

performs the relevant preprocessing, including replacing words occurring less than a minimum frequency cutoff with the <unk> symbol.

Training and testing

Neural parser:

Create an 'oxdp.ini' file with the following contents:

iterations=20
minibatch-size=128
randomise=true
lambda-lbl=10.0
representation-size=256
step-size=0.05
diagonal-contexts=false
activation=sigmoid
threads=4
labelled-parser=true
lexicalised=true
predict-pos=true
tag-pos=false
label-features=true
distance-features=false
morph-features=false
parser-type=arcstandard
context-type=more-extended
direction-det=false
sum-over-beam=false
root-first=true
bootstrap=false
bootstrap-iter=0
complete-parse=true
max-beam-increment=100
num-particles=100
generate-samples=0

Partition the vocabulary using agglomerative Brown clustering:

brown-cluster/wcluster --c num-clusters \
                       --threads=10 \
                       --text training.unk.en \
                       --output_dir=clusters

Set num-clusters to sqrt(vocabulary_size).

Train the model:

oxdp/bin/train_sgd -c oxdp.ini -i train.conll --test-set dev.conll --test-set2 test.conll --test-out-file test.out.conll --model-out model.bin --class-file clusters/paths

Neural syntactic language modelling:

Create an 'oxdp.ini' file with the following contents:

iterations=20
minibatch-size=128
randomise=true
lambda-lbl=10.0
representation-size=256
step-size=0.05
diagonal-contexts=false
activation=sigmoid
noise-samples=0
threads=4
labelled-parser=false
lexicalised=true
parser-type=arcstandard
context-type=extended-with-ngram
predict-pos=false
tag-pos=false
label-features=false
distance-features=false
morph-features=false
direction-det=true
sum-over-beam=false
root-first=true
bootstrap=false
bootstrap-iter=5
complete-parse=false
max-beam-increment=1
num-particles=1000
generate-samples=0

Train the model as before.

Bayesian parser:

Create an 'oxdp.ini' file with the following contents:

iterations=20
minibatch-size=1
minibatch-size-unsup=1
randomise=true
labelled-parser=true
parser-type=arcstandard
lexicalised=true
sum-over-beam=false
semi-supervised=false
direction-det=false
particle-resample=true
num-particles=100
max-beam-size=256

Train the model:

oxdp/bin/train_gibbs -c oxdp.ini -i train.conll --test-set dev.conll --test-output-file out.conll

Citation

If you use this code, please please cite one of the following papers:

  1. Generative Incremental Dependency Parsing with Neural Networks - Jan Buys and Phil Blunsom. ACL 2015. Paper.

  2. A Bayesian Model for Generative Transition-based Dependency Parsing - Jan Buys and Phil Blunsom. Depling 2015. Paper.

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Generative transition-based dependency parsing models.

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