PhoNLP: A joint multi-task learning model for Vietnamese part-of-speech tagging, named entity recognition and dependency parsing

PhoNLP is a multi-task learning model for joint part-of-speech (POS) tagging, named entity recognition (NER) and dependency parsing. Experiments on Vietnamese benchmark datasets show that PhoNLP produces state-of-the-art results, outperforming a single-task learning approach that fine-tunes the pre-trained Vietnamese language model PhoBERT for each task independently.

Details of the PhoNLP model architecture and experimental results can be found in our following paper:

@article{PhoNLP,
title     = {{PhoNLP: A joint multi-task learning model for Vietnamese part-of-speech tagging, named entity recognition and dependency parsing}},
author    = {Linh The Nguyen and Dat Quoc Nguyen},
journal   = {arXiv preprint},
volume    = {arXiv:2101.01476},
year      = {2021}
}

Please CITE our paper when PhoNLP is used to help produce published results or incorporated into other software.

Although we specify PhoNLP for Vietnamese, usage examples below in fact can directly work for other languages that have gold annotated corpora available for the three tasks of POS tagging, NER and dependency parsing, and a pre-trained BERT-based language model available from transformers.

Installation

• Python version >= 3.6; PyTorch version >= 1.4.0
• PhoNLP can be installed using pip as follows: pip3 install phonlp
• Or PhoNLP can also be installed from source with the following commands:

   git clone https://github.com/VinAIResearch/PhoNLP
   cd PhoNLP
   pip3 install -e .
  

Usage example: Command lines

To play with the examples using command lines, please install phonlp from the source:

git clone https://github.com/VinAIResearch/PhoNLP
cd PhoNLP
pip3 install -e . 

Training

cd phonlp/models
python3 run_phonlp.py --mode train --save_dir  \
	--pretrained_lm  \
	--lr  --batch_size  --num_epoch  \
	--lambda_pos  --lambda_ner  --lambda_dep  \
	--train_file_pos  --eval_file_pos  \
	--train_file_ner  --eval_file_ner  \
	--train_file_dep  --eval_file_dep 

--lambda_pos, --lambda_ner and --lambda_dep represent mixture weights associated with POS tagging, NER and dependency parsing losses, respectively, and lambda_pos + lambda_ner + lambda_dep = 1.

Example:

cd phonlp/models
python3 run_phonlp.py --mode train --save_dir ./phonlp_tmp \
	--pretrained_lm "vinai/phobert-base" \
	--lr 1e-5 --batch_size 32 --num_epoch 40 \
	--lambda_pos 0.4 --lambda_ner 0.2 --lambda_dep 0.4 \
	--train_file_pos ../sample_data/pos_train.txt --eval_file_pos ../sample_data/pos_valid.txt \
	--train_file_ner ../sample_data/ner_train.txt --eval_file_ner ../sample_data/ner_valid.txt \
	--train_file_dep ../sample_data/dep_train.conll --eval_file_dep ../sample_data/dep_valid.conll

Evaluation

cd phonlp/models
python3 run_phonlp.py --mode eval --save_dir  \
	--batch_size  \
	--eval_file_pos  \
	--eval_file_ner  \
	--eval_file_dep  

Example:

cd phonlp/models
python3 run_phonlp.py --mode eval --save_dir ./phonlp_tmp \
	--batch_size 8 \
	--eval_file_pos ../sample_data/pos_test.txt \
	--eval_file_ner ../sample_data/ner_test.txt \
	--eval_file_dep ../sample_data/dep_test.conll 

Annotate a corpus

cd phonlp/models
python3 run_phonlp.py --mode annotate --save_dir  \
	--batch_size  \
	--input_file  \
	--output_file  

Example:

cd phonlp/models
python3 run_phonlp.py --mode annotate --save_dir ./phonlp_tmp \
	--batch_size 8 \
	--input_file ../sample_data/input.txt \
	--output_file ../sample_data/output.txt 

The pre-trained PhoNLP model for Vietnamese is available at HERE!

Usage example: Python API

import phonlp
# Automatically download the pre-trained PhoNLP model 
# and save it in a local machine folder
phonlp.download(save_dir='./pretrained_phonlp')
# Load the pre-trained PhoNLP model
model = phonlp.load(save_dir='./pretrained_phonlp')
# Annotate a corpus where each line represents a word-segmented sentence
model.annotate(input_file='input.txt', output_file='output.txt')
# Annotate a word-segmented sentence
model.print_out(model.annotate(text="Tôi đang làm_việc tại VinAI ."))

By default, the output for each input sentence is formatted with 6 columns representing word index, word form, POS tag, NER label, head index of the current word and its dependency relation type:

1	Tôi	P	O	3	sub	
2	đang	R	O	3	adv
3	làm_việc	V	O	0	root
4	tại	E	O	3	loc
5	VinAI	Np 	B-ORG	4	prob
6	.	CH	O	3	punct

In addition, the output can be formatted following the 10-column CoNLL format where the last column is used to represent NER predictions. This can be done by adding output_type='conll' into the model.annotate() function. Also, in the model.annotate() function, the value of the parameter batch_size can be adjusted to fit your computer's memory instead of using the default one at 1 (batch_size=1). Here, a larger batch_size would lead to a faster performance speed.