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Contrastive Self-Supervised Learning for Commonsense Reasoning

News

07/17/2020: Provided source code
05/15/2020: Provided model for download

This repository contains the source code for our paper Contrastive Self-Supervised Learning for Commonsense Reasoning to be presented at ACL2020. The code is in parts based on the code from Huggingface Tranformers and the paper A Surprisingly Robust Trick for Winograd Schema Challenge.

Schematic Illustration of MEx

We propose a self-supervised method to solve Pronoun Disambiguation and Winograd Schema Challenge problems. Our approach exploits the characteristic structure of training corpora related to so-called trigger words, which are responsible for flipping the answer in pronoun disambiguation. We achieve such commonsense reasoning by constructing pair-wise contrastive auxiliary predictions. To this end, we leverage a mutual exclusive loss regularized by a contrastive margin. Our architecture is based on the recently introduced transformer networks, BERT, that exhibits strong performance on many language understanding benchmarks. Empirical results show that our method alleviates the limitation of current supervised approaches for commonsense reasoning. This study opens up avenues for exploiting inexpensive self-supervision to achieve performance gain in commonsense reasoning tasks.

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/acl2020-commonsense-reasoning
cd acl2020-commonsense-reasoning
pip install -r requirements.txt

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

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
cd ..

Training and evaluating the model

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 4 --learning_rate 1.0e-5 --alpha_param 0.05 --beta_param 0.02 --num_train_epochs 25.0 --output_dir model_output/ --gamma_param 60.0 --shuffle

(optional) Evaluating the model without training

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

Model

The BERT-Large model is available in the Huggingface repository as sap-ai-research/BERT-Large-Contrastive-Self-Supervised-ACL2020.

Loading the model in Python:

tokenizer = AutoTokenizer.from_pretrained("sap-ai-research/BERT-Large-Contrastive-Self-Supervised-ACL2020")

model = AutoModelWithLMHead.from_pretrained("sap-ai-research/BERT-Large-Contrastive-Self-Supervised-ACL2020")

This model should reproduce the results reported in the paper:

Knowref-test:  0.6558966074313409
DPR/WSCR-test:  0.8014184397163121
WSC:  0.6959706959706959
PDP:  0.9

Related work

See our work accepted ACL'19 - Attention Is (not) All You Need for Commonsense Reasoning - proposing BERT attention-guidance for commonsense reasoning. arXiv, GitHub

Known Issues

No issues known

How to obtain support

This project is provided "as-is" and any bug reports are not guaranteed to be fixed.

Citations

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

@inproceedings{klein-nabi-2020-contrastive,
    title = "Contrastive Self-Supervised Learning for Commonsense Reasoning",
    author = "Klein, Tassilo  and
      Nabi, Moin",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.671",
    pages = "7517--7523"
}