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Adversarial Attack and Defense of Structured Prediction Models

Source codes used in EMNLP 2020 paper, Adversarial Attack and Defense of Structured Prediction Models.

Requirements

  • python 2 & python 3
  • anaconda
  • pytorch 0.4.1 & pytorch >= 1.0
  • transformers
  • gensim
  • numpy
  • bert-score
  • pytorch-pretrained-bert
  • nltk

Configuration

  1. Clone this repository and two anaconda environments: one is python 2 and the other is python 3

  2. For python 2 env, install pytorch 0.4.1, gensim, numpy, nltk.
    For python 3 env, install pytorch >= 1.0, transformers, bert-score, pytorch-pretrained-bert and numpy
    You also can download the python2 enviroments from here.

  3. Downloading sskip embedding and conllu format PTB dataset.

Dependency Parsing

  1. pretrain parser model. Or you can download our pretrained model here: biaffine, stackptr, bist
# pretrain victim model
$ sh examples/run_graphParser.sh

# pretrain reference parser stackPtr
$ sh examples/run_stackPtrParser.sh

# pretrain reference parser bist
$ cd bist_parser
$ sh test.sh
  1. Move biaffine parser and stackPtr parser to ./models/parsing/{biaffine, stack_ptr}. Move pretrained bist parser to bist_parser/pretrained/model1.
  2. Pretrain seq2seq sentence generator. Or you also can get our trained seq2sq model here.
$ /path/to/python2/envs/python examples/pretrain_seq2seq.py --cuda --mode LSTM \
--num_epochs 30 --batch_size 64 --hidden_size 512 --num_layers 3 --pos_dim 100 --char_dim 100 --num_filters 100 \
--learning_rate 1e-3 --decay_rate 0.05 --schedule 5 --gamma 0.0 \
--p_in 0.33 --p_rnn 0.33 0.5 --p_out 0.5 \
--word_embedding sskip --word_path data/sskip/sskip.eng.100.gz \
--train data/ptb/train.conllu \
--dev data/ptb/dev.conllu \
--test data/ptb/test.conllu \
--char_embedding random \
--model_path models/parsing/biaffine
  1. RL training:
$ sh examples/run_rl_graph_parser.py
  1. You also can download our trained model here. And run eval to get results reported in paper.
$ sh examples/eval_rl_graph_parser.py

POS Tagging

  1. Pretrain the victim model. Or you also can get our pretrained version here.
$ sh run_posCRFTagger.sh
  1. Download and unzip reference parser: stanford-postagger, senna.
  2. Pretrain the seq2seq model. Or you also can download our pretrained version here
/path/to/python2/envs/python examples/pretrain_seq2seq.py --cuda --mode LSTM \
--num_epochs 30 --batch_size 64 --hidden_size 256 --num_layers 1 --char_dim 30 --num_filters 30 \
--learning_rate 1e-3 --decay_rate 0.05 --schedule 5 --gamma 0.0 \
--p_in 0.33 --p_rnn 0.33 0.5 --p_out 0.5 \
--word_embedding sskip --word_path data/sskip/sskip.eng.100.gz \
--train data/ptb/train.conllu \
--dev data/ptb/dev.conllu \
--test data/ptb/test.conllu \
--char_embedding random \
--model_path path/to/victim/model/
  1. RL training.
$ sh examples/run_adv_tagger.sh
  1. You also can download our trained model here. And run eval to get results reported in paper.
$ sh examples/eval_adv_tagger.py