Source code for paper Effective Unsupervised Domain Adaptation with Adversarially Trained Language Models

• WNUT2016 Data
• CoNLL2003 Data Other (eng.testa - dev, eng.testb - test) Base on Reuter newswire data (English and German)` Labels: ORG, PER, LOC, MISC Format:
• Financial NER Data FIN3: 3 financial agreement documents FIN5: 5 financial agreement documents
• WNUT2017 Data
• BioNER Data
  • Anatomy: AnatEM, CRAFT-anatomy
  • Gene/Protein: BC2GM, BioNLP09, BioNLP11EPI, BioNLP13GE, Ex-PTM, JNPBA
  • Chemical: BC4CHEMD, BC5CDR-chem, CRAFT, BIONLP13CG

All experiment scripts can be found under experiments directory

Build pretraining dataset

CORPUS_DIR=
VOCAB_FILE=$BERT_BASE_CASED/vocab.txt
OUT_DIR=
MAX_SEQ_LEN=128
NUM_OUT_FILES=50
python3 build_pretraining_dataset.py \
    --corpus-dir=$CORPUS_DIR \
    --vocab-file=$VOCAB_FILE \
    --output-dir=$OUT_DIR \
    --max-seq-length=$MAX_SEQ_LEN \
    --num-processes=1 \
    --blanks-separate-docs=False \
    --num-out-files=$NUM_OUT_FILES

Task config json files can be found under experiments/config/domain-tuning directory

DATA_DIR=
CONFIG_FILE=fin_noext_adv.json
MODEL_NAME=
python3 run_pretraining.py \
  --data-dir=$DATA_DIR \
  --hparams=$CONFIG_FILE \
  --model-name=$MODEL_NAME

Task config json files can be found under experiments/config/ directory

MODEL_CONFIG=fin_span_finetune.json
CHECKPOINT=
python3 run_finetuning.py \
    --data-dir=$DATA_DIR \
    --hparams=$MODEL_CONFIG \
    --model-name='ner' \
    --init-checkpoint=$CHECKPOINT

Please cite the following paper if you found the resources in this repository useful.

@inproceedings{vu-etal-2020-effective,
    title = "Effective Unsupervised Domain Adaptation with Adversarially Trained Language Models",
    author = "Vu, Thuy-Trang  and
      Phung, Dinh  and
      Haffari, Gholamreza",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2020.emnlp-main.497",
    doi = "10.18653/v1/2020.emnlp-main.497",
    pages = "6163--6173"
}

This project is implemented based on electra source code