Official code for the paper "Deep Contextualized Self-training for Low Resource Dependency Parsing".
If you use this code please cite our paper.

Simply run:

• Python 3.7
• Pytorch 1.1.0
• Cuda 10.0

pip install -r requirements.txt

Preprocessed in note format. Data folder can be obtained from here.

Set the word_path="./data/morph.word.200.vec" and char_path="./data/morph.char.30.vec" Embeddings can be found here Possible word embedding option: ['random', 'fasttext']
The multilingual word embedding (.vec extensions) should be placed under the data/multilingual_word_embeddings folder.

In order to run the low resource in-domain experiments there are three steps we need to follow:

1. Running the base Biaffine parser
2. Running the sequence tagger(s)
3. Running the combined DCST parser

Create saved_model empty folder to store the new models. If you want to run complete model then simply run bash script run_dcsh.sh otherwise Refer to corrsoponding section in run_dcsh.sh to run corrsopnding segments.

1. Without POS Tag : Don't use flag --use_pos for all stages, namely, base model, auxiliary tasks, Final ensembled model.
2. With Coarse level Tag : Use the input files from data folder from --use_pos flag here
3. With POS level Tag : Shuffle 2nd and 3rd column of all the files in data folder.

Add pretrained model file from Here to path ./utils/morph_tagger/cwlm_lstm_crf_cas_2.model

1. Pretrained morph tagger layer : Run the run_dcst.sh.
2. Pretrained morph tagger layer with freezing: comment this and then run run_dcst.sh