ashim95 / DCST

DCST with Mullti-task setting

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

Requirements

Simply run:

• Python 3.7
• Pytorch 1.1.0
• Cuda 10.0

pip install -r requirements.txt

Data

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

Multilingual Word Embeddings

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.

Low Resource In-domain Experiments

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

Running code

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.

Input settings:

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 with --use_pos flag here
3. With POS level Tag : Shuffle 2nd and 3rd column of all the files in data folder.

Mode : Average

Use average of FastText, case-layer and nos-layer hidden representation as embedding. Set the word_path="./data/cc.sanskrit.300.case.nos.vec" or cc.sanskrit.300.FT.case.nos.vec or cc.sanskrit.300.case.vec. These files can be found here

Running the base Biaffine Parser

Note that to run BiAFF classifier on 500 training data set --set_num_training_samples 500. And if you want to train on complete trainind data remove this flag. Refer to corrsoponding section in run_dcsh.sh

Running the Sequence Tagger

Once training the base parser, we can now run the Sequnece Tagger on any of the three proposed sequence tagging tasks in order to learn the syntactical contextualized word embeddings from the unlabeled data set. \

1. For Auxiliary task set tasks as : 'number_of_children' 'relative_pos_based' 'distance_from_the_root'
2. For Multitask setting set tasks : 'Multitask_case_predict' 'Multitask_POS_predict' 'Multitask_label_predict'

Refer to corrsoponding section in run_dcsh.sh

Final step - Running the Combined DCST Parser

As a final step we can now run the DCST (ensemble) parser:

Refer to corrsoponding section in run_dcsh.sh