The repository of our EMNLP'20 paper
SeqMix: Augmenting Active Sequence Labeling via Sequence Mixup
[paper] [slides]

• pytorch-transformers==1.2.0
• torch==1.2.0
• seqeval==0.0.5
• tqdm==4.31.1
• nltk==3.4.5
• Flask==1.1.1
• Flask-Cors==3.0.8
• pytorch_pretrained_bert==0.6.2

Install the required packages:

pip install -r requirements.txt

• data_dir: specify the data file, we provide CoNLL-03 dataset here
• max_seq_length: maximum length of each sequence
• num_train_epochs: number of training epochs
• train_batch_size: batch size during model training
• active_policy: query policy of active learning
• augment_method: augmenting method
• augment_rate: augmenting rate
• hyper_alpha: parameter of Beta distribution

Random Sampling

python active_learn.py --active_policy=random

Least Confidence Sampling

python active_learn.py --active_policy=lc

Normalized Token Entropy sampling

python active_learn.py --active_policy=nte

Whole sequence mixup

python active_learn.py --augment_method=soft

Sub-sequence mixup

python active_learn.py --augment_method=slack

Label-constrained sub-sequence mixup

python active_learn.py --augment_method=lf