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The repository for our EMNLP'20 paper SeqMix: Augmenting Active Sequence Labeling via Sequence Mixup.

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SeqMix

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

Illustration of the three variants of SeqMix

Requirements

  • 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

Key Parameters

  • 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

Run

Active learning part

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

Seqmix part

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

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The repository for our EMNLP'20 paper SeqMix: Augmenting Active Sequence Labeling via Sequence Mixup.

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