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README.md

Gendered Ambiguous Pronouns (GAP) - ProBERT and GREP

This repo contains code for the paper Gendered Ambiguous Pronouns Shared Task: Boosting Model Confidence by Evidence Pooling

and the winning model in the Kaggle competition Gendered Pronoun Resolution

If you use this code for your research, please cite the paper

Abstract: The paper presents a strong set of results for resolving gendered ambiguous pronouns on the Gendered Ambiguous Pronouns shared task. The model presented draws upon the strengths of state-of-the-art language and coreference resolution models, and introduces a novel evidence-based deep learning architecture. Injecting evidence from the coreference models compliments the base architecture, and analysis shows that the model is not hindered by their weaknesses, specifically gender bias. The modularity and simplicity of the architecture make it very easy to extend for further improvement and applicable to other NLP problems. Evaluation on GAP test data results in a state-of-the-art performance at 92.5% F1 (gender bias of 0.97), edging closer to the human performance of 96.6%. The end-to-end solution placed 1st in the Kaggle competition, winning by a significant lead.

The code for ProBERT and GREP model architectures is located under models/gap/.

Setup

Hardware/Platform Specs

The models were trained using 4 V100 gpus. It is possible to train the models on a single gpu to get a comparable performance by adjusting the batch size accordingly.

All models were developed and tested in

python3.6

Dependencies

Most of the dependencies are listed in requirement.txt and can be installed by

pip install requirements.txt

Note that this file was generated automatically and you may need to resolve certain dependencies manually.

Download

Coref models need the following external data to be downloaded and placed in externals/data/

curl -O https://nlp.stanford.edu/data/glove.840B.300d.zip
unzip glove.840B.300d.zip
rm glove.840B.300d.zip

curl -O https://s3-us-west-2.amazonaws.com/allennlp/models/biaffine-dependency-parser-ptb-2018.08.23.tar.gz
curl -O https://s3-us-west-2.amazonaws.com/allennlp/models/coref-model-2018.02.05.tar.gz

Lee et all e2e-coref model checkpoints:

Download pretrained models at https://drive.google.com/file/d/1fkifqZzdzsOEo0DXMzCFjiNXqsKG_cHi
Move the downloaded file to externals/data and extract: tar -xzvf e2e-coref.tgz

Refer https://github.com/kentonl/e2e-coref for additional configuration, if needed.

Stanford CoreNLP package needs to be downloaded and placed in externals/

curl -O http://nlp.stanford.edu/software/stanford-corenlp-full-2018-10-05.zip
unzip stanford-corenlp-full-2018-10-05.zip
rm stanford-corenlp-full-2018-10-05.zip

Data

The GAP dataset along with the corrections is included in this repo in the 'data' folder.

Preprocessing

python run.py --preprocess_train 
              --model=grep 
              --language_model=bert-large-uncased 
              --coref_models=url,allen,hug,lee 
              --exp_dir=results/grep

Training

Trained models are not included as part of the archive due to their large size.

Models can be trained by executing the following command from project root

python run.py --train 
              --model=grep 
              --language_model=bert-large-uncased 
              --coref_models=url,allen,hug,lee 
              --exp_dir=results/grep

Prediction

The trained models can used for prediction by running the code in predict mode

python run.py --predict 
              --preprocess_eval 
              --model=grep 
              --language_model=bert-large-uncased 
              --coref_models=url,allen,hug,lee 
              --exp_dir=results/grep

Kaggle submission

To reproduce kaggle submission results

!python run.py --train 
               --predict
               --kaggle 
               --preprocess_train 
               --preprocess_eval 
               --model=grep 
               --language_model=bert-large-uncased 
               --coref_models=url,allen,hug,lee 
               --verbose=1 
               --exp_dir=results/kaggle 
               --test_path=data/test_stage_2.tsv 
               --sub_sample_path=sample_submission_stage_2.csv

NOTE: It is possible to use appropriate flags and run the pipeline end to end in a single execution process. The above example runs the pipeline end-2-end. The only difference in --kaggle mode is that the model gets trained over all the GAP data and the predictions get averaged over multiple folds, seeds and language model versions.

usage.ipynb contains example usage of the pipeline.

Additional Resources

Visualization

AllenNLP style visualizations for GAP, Stanford CoreNLP, Huggingface NeuralCoref and Lee et al. e2e-coref. The implementation is located in visualization/

See jupyter notebook at https://www.kaggle.com/sattree/1-coref-visualization-jupyter-allenlp-stanford

GAP Heuristics

An implementation of GAP heurisitcs can be found in models/heuristics/

See jupyter notebook at https://www.kaggle.com/sattree/2-reproducing-gap-results

Pretrained Coref Models

Unified interface for using pretrained coref models can be found in models/pretrained/

See jupyter notebook at https://www.kaggle.com/sattree/3-a-better-baseline

BibTex

@inproceedings{attree-2019-gendered,
    title = "Gendered Ambiguous Pronouns Shared Task: Boosting Model Confidence by Evidence Pooling",
    author = "Attree, Sandeep",
    booktitle = "Proceedings of the First Workshop on Gender Bias in Natural Language Processing",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3820",
    doi = "10.18653/v1/W19-3820",
    pages = "134--146",
    abstract = "This paper presents a strong set of results for resolving gendered ambiguous pronouns on the Gendered Ambiguous Pronouns shared task. The model presented here draws upon the strengths of state-of-the-art language and coreference resolution models, and introduces a novel evidence-based deep learning architecture. Injecting evidence from the coreference models compliments the base architecture, and analysis shows that the model is not hindered by their weaknesses, specifically gender bias. The modularity and simplicity of the architecture make it very easy to extend for further improvement and applicable to other NLP problems. Evaluation on GAP test data results in a state-of-the-art performance at 92.5{\%} F1 (gender bias of 0.97), edging closer to the human performance of 96.6{\%}. The end-to-end solution presented here placed 1st in the Kaggle competition, winning by a significant lead.",
}
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