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Neural Based DMV

Python reimplementations of a series of neural extensions of the Dependency Model with Valence (DMV) for unsupervised dependency parsing.

Due to the difference in the framework, dataset processing methods and model implementation, we did not use the parameter settings in the paper and results are for reference only.

Requirements

python==3.7
pytorch==1.4
cupy-cuda100==7.2.0

A GPU is required.

NDMV

model result in paper configure file
Neural DMV (Standard EM) 55.4 51.3 ndmv_em_1.json
Neural DMV (Viterbi EM) 65.1 65.9 ndmv_viterbi_1.json
Neural E-DMV 69.0 69.7 ndmv_viterbi_2.json
Neural E-DMV (Good Init) - 72.5 -

HOW TO RUN

  1. Prepare your dataset. CoNLL format are expected and train, dev and test are splitted.
  2. Modify train_ds, dev_ds and test_ds fields in the configure file.
  3. run command python model/ndmv.py --load_option <path_to_configure_file>.

You can find the description of each field of the configuration file in the subclass of Options, which named as XXXXOptions.

If get an ModuleNotFoundError for utils or module, you need set PYTHONPATH to include the project`s root.

L-NDMV