A Surprisingly Robust Trick for Winograd Schema Challenge and WikiCREM: A Large Unsupervised Corpus for Coreference Resolution

This code contains models and experiments for the paper A Surprisingly Robust Trick for Winograd Schema Challenge and WikiCREM: A Large Unsupervised Corpus for Coreference Resolution.

The MaskedWiki datasets and pre-trained models can be downloaded from this webpage. The link contains two datasets, MaskedWiki_Sample (~2.4M examples) and MaskedWiki_Full (~130M examples). All the experiments were conducted with the MaskedWiki_Sample only.

The WikiCREM datasets and BERT_WikiCREM model can be downloaded from this webpage.

The following libraries are needed to run the code: Python 3 (version 3.6 or later), numpy (version 1.14 was used), pytorch (version 0.4.1 was used), tqdm, boto3, nltk (version 3.3 was used), requests, Spacy (version 2.0.13 was used), Spacy en_core_web_lg model.

If you evaluate any of the models on the GAP dataset, we encourage you to check out this project which addresses imbalances within its bias metric.

To evaluate BERT on all datasets, use the following script:

python main.py \
      --task_name wscr \
      --do_eval \
      --eval_batch_size 10 \
      --data_dir "data/" \
      --bert_model bert-large-uncased \
      --max_seq_length 128 \
      --output_dir model_output/

To evaluate one of the downloaded pre-trained models, use the following code:

python main.py \
      --task_name wscr \
      --do_eval \
      --eval_batch_size 10 \
      --data_dir "data/" \
      --bert_model bert-large-uncased \
      --max_seq_length 128 \
      --output_dir model_output/ \
      --load_from_file models/BERT_Wiki_WscR

To train the BERT_Wiki model, use the code below. To reproduce the exact results from the paper, use the versions of the libraries as listed in the conda environment wsc_env.yml. Please note that re-training the models with different version of the libraries may yield different results. Running a full hyper-parameter search is recommended in this case.

python main.py \
      --task_name maskedwiki \
      --do_eval \
      --do_train \
      --eval_batch_size 10 \
      --data_dir "data/" \
      --bert_model bert-large-uncased \
      --max_seq_length 128 \
      --train_batch_size 64 \
      --alpha_param 20 \
      --beta_param 0.2 \
      --learning_rate 5.0e-6 \
      --num_train_epochs 1.0 \
      --output_dir model_output/

To train the BERT_Wiki_WscR model, download the MaskedWiki_sample into the data folder and BERT_Wiki model into the models folder. Then use the following code:

python main.py \
      --task_name wscr \
      --do_eval \
      --do_train \
      --eval_batch_size 10 \
      --data_dir "data/" \
      --bert_model bert-large-uncased \
      --max_seq_length 128 \
      --train_batch_size 64 \
      --learning_rate 1.0e-5 \
      --alpha_param 5 \
      --beta_param 0.2 \
      --num_train_epochs 30.0 \
      --output_dir model_output/ \
      --load_from_file models/BERT_Wiki

References

@inproceedings{kocijan19acl,
    title     = {A Surprisingly Robust Trick for Winograd Schema Challenge},
    author    = {Vid Kocijan and
               Ana-Maria Cretu and
               Oana-Maria Camburu and
               Yordan Yordanov and
               Thomas Lukasiewicz},
    booktitle = {The 57th Annual Meeting of the Association for Computational Linguistics (ACL)},
    address = {Florence, Italy},
    month = {July},
    year = {2019}
}

@inproceedings{kocijan19emnlp,
    title     = {WikiCREM: A Large Unsupervised Corpus for Coreference Resolution},
    author    = {Vid Kocijan and
               Ana-Maria Cretu and
               Oana-Maria Camburu and
               Yordan Yordanov and
               Phil Blunsom and
               Thomas Lukasiewicz},
    booktitle = {Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
    address = {Hong Kong},
    month = {November},
    year = {2019}
}

mustafaalisaba/bert-commonsense

A Surprisingly Robust Trick for Winograd Schema Challenge and WikiCREM: A Large Unsupervised Corpus for Coreference Resolution

References