Attention-based Contrastive Learning for Winograd Schemas

News

11/24/2021: Provided source code

In this repository we will provide the source code for the paper Attention-based Contrastive Learning for Winograd Schemas to be presented at EMNLP 2021. The code is in parts based on the code from Huggingface Tranformers and the paper A Surprisingly Robust Trick for Winograd Schema Challenge.

Abstract

Self-supervised learning has recently attracted considerable attention in the NLP community for its ability to learn discriminative features using a contrastive objective. This paper investigates whether contrastive learning can be extended to Transfomer attention to tackling the Winograd Schema Challenge. To this end, we propose a novel self-supervised framework, leveraging a contrastive loss directly at the level of self-attention. Experimental analysis of our attention-based models on multiple datasets demonstrates superior commonsense reasoning capabilities. The proposed approach outperforms all comparable unsupervised approaches while occasionally surpassing supervised ones.

Authors:

Requirements

Python (version 3.6 or later)
PyTorch
Huggingface Tranformers

Download and Installation

Install the requiremennts:

conda install --yes --file requirements.txt

pip install -r requirements.txt

Clone this repository and install dependencies:

git clone https://github.com/SAP-samples/emnlp2021-attention-contrastive-learning
cd emnlp2021-attention-contrastive-learning
pip install -r requirements.txt

Create 'data' sub-directory and download files for PDP, WSC challenge, KnowRef, DPR and WinoGrande:

mkdir data
wget https://cs.nyu.edu/faculty/davise/papers/WinogradSchemas/PDPChallenge2016.xml
wget https://cs.nyu.edu/faculty/davise/papers/WinogradSchemas/WSCollection.xml
wget https://raw.githubusercontent.com/aemami1/KnowRef/master/Knowref_dataset/knowref_test.json
wget http://www.hlt.utdallas.edu/~vince/data/emnlp12/train.c.txt
wget http://www.hlt.utdallas.edu/~vince/data/emnlp12/test.c.txt
wget https://storage.googleapis.com/ai2-mosaic/public/winogrande/winogrande_1.1.zip
unzip winogrande_1.1.zip
rm winogrande_1.1.zip
cd ..

Train

python main-AMEX.py --do_train --do_eval --learning_rate 1.0e-5 --shuffle --ignore_bestacc --loadcachedfeatures --train_batch_size 18 --num_train_epochs 22 --eval_batch_size 10 --max_seq_length 40 --alpha_param 0.05 --beta_param 0.02 --lambda_param 1.0 --warmup_steps 150  --bert_model=roberta-large --cache_dir cache/ --data_dir=data/  --output_dir model_output/ --task_name wscr

How to obtain support

Create an issue in this repository if you find a bug or have questions about the content. For additional support, ask a question in SAP Community.

Citations

If you use this code in your research, please cite:

@inproceedings{klein-nabi-2021-attention-based,
    title = "Attention-based Contrastive Learning for {W}inograd Schemas",
    author = "Klein, Tassilo  and
      Nabi, Moin",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
    month = nov,
    year = "2021",
    address = "Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.findings-emnlp.208",
    pages = "2428--2434"
}

SAP-samples/emnlp2021-attention-contrastive-learning