BERTAC (BERT-style transformer-based language model with Adversarially pretrained Convolutional neural network)

BERTAC is a framework that combines a Transformer-based Language Model (TLM) such as BERT with an adversarially pretrained CNN (Convolutional Neural Network). It was proposed in our ACL-IJCNLP paper:

• BERTAC: Enhancing Transformer-based Language Models with Adversarially Pretrained Convolutional Neural Networks.

We showed in our experiments that BERTAC can improve the performance of TLMs on GLUE and open-domain QA tasks when using ALBERT or RoBERTa as the base TLM.

This repository provides the source code for BERTAC and adversarially pretrained CNN models described in the ACL-IJCNLP 2021 paper.

You can download the code and CNN models by following the procedure described in the "Try BERTAC section." The procedure includes downloading the BERTAC code, installing libraries required to run the code, and downloading pretrained models of the fastText word embedding vectors, the ALBERT xxlarge model, and our adversarially pretrained CNNs. The CNNs provided here were pretrained using the settings described in our ACL-IJCNLP 2021 paper. They can be downloaded automatically by running the script download_pretrained_model.sh as described in the "Try BERTAC section" or manually from the following page: cnn_models/README.md.

After this is done, you can run the GLUE and Open-domain QA experiments in the ACL-IJCNLP 2021 paper by following the procedure described in these pages, examples/GLUE/README.md and examples/QA/README.md. The procedure for the experiments starts from downloading GLUE and open-domain QA datasets (Quasar-T and SearchQA datasets for open-domain QA) and includes preprocessing the dataset and training/evaluating BERTAC models.

Overview of BERTAC

BERTAC is designed to improve Transformer-based Language Models such as ALBERT and BERT by integrating a simple CNN to them. The CNN is pretrained in a GAN (Generative Adversarial Network) style using Wikipedia data. By using as training data sentences in which an entity was masked in a cloze-test style, the CNN can generate alternative entity representations from sentences. BERTAC aims to improve TLMs for a variety of downstream tasks by using multiple text representations computed from different perspectives, i.e., those of TLMs trained by masked language modeling and those of CNNs trained in a GAN style to generate entity representations.

For a technical description of BERTAC, see our paper:

• Jong-Hoon Oh, Ryu Iida, Julien Kloetzer, Kentaro Torisawa, BERTAC: Enhancing Transformer-based Language Models with Adversarially Pretrained Convolutional Neural Networks

Try BERTAC

Prerequisites

BERTAC requires the following libraries and tools at runtime.

• CUDA: A CUDA runtime must be available in the runtime environment. Currently, BERTAC has been tested with CUDA 10.1 and 10.2.
• Python and Pytorch: BERTAC has been tested with Python 3.6 and 3.8, and Pytorch 1.5.1 and 1.8.1.
• Perl: BERTAC has been tested with Perl 5.16.1 and 5.26.2.

Installation

You can install BERTAC by following the procedure described below.

• Create a new conda environment bertac using the following command. Set a CUDA version available in your environment.

conda create -n bertac python=3.8 tqdm requests scikit-learn cudatoolkit cudnn lz4

• Install Pytorch into the conda environment

conda activate bertac
conda install -n bertac pytorch=1.8 -c pytorch

• Git clone the BERTAC code and run pip install -r requirements.txt in the root directory.

# git clone the code
git clone https://github.com/nict-wisdom/bertac
cd bertac

# Install requirements
pip install -r requirements.txt

• Download the spaCy model en_core_web_md.

# Download the spaCy model 'en_core_web_md' 
python -m spacy download en_core_web_md

• Install Perl and its JSON module into the conda environment.

# Install Perl and its JSON module
conda install -c anaconda perl -n bertac38
cpan install JSON

• Download the fastText word embedding vectors, the ALBERT xxlarge model, and our adversarially pretrained CNNs. The CNNs were pretrained using the settings described in our ACL-IJCNLP 2021 paper (See cnn_models/README.md for the details on our pretrained CNNs).

# Download pretrained CNN models, the fastText word embedding vectors, and
# the ALBERT xxlarge model (albert-xxlarge-v2) 
sh download_pretrained_model.sh

Note: the BERTAC code was built on the HuggingFace Transformers v2.4.1 and requires the NVIDIA apex as in the HuggingFace Transformers. Please install the NVIDIA apex following the procedure described in the NVIDIA apex page.

You can enter examples/GLUE or examples/QA folders and try the bash commands under these folders to run GLUE or open-domain QA experiments (see examples/GLUE/README.md and examples/QA/README.md for details on the procedures of the experiments).

GLUE experiments

You can run GLUE experiments by following the procedure described in examples/GLUE/README.md.

Results

The performances of BERTAC and other baseline models on the GLUE development set are shown below.

BERTAC(ALBERT-xxlarge), i.e., BERTAC using ALBERT-xxlarge as its base TLM, showed a higher average score (Avg. of the last column in the table) than (1) ALBERT-xxlarge (the base TLM) and (2) DeBERTa-large (the state-of-the-art method for the GLUE development set).

Open-domain QA experiments

You can run open-domain QA experiments by following the procedure described in examples/QA/README.md.

Results

The performances of BERTAC and other baseline methods on Quasar-T and SearchQA benchmarks are as follows.

Here, BERTAC(RoBERTa-large) and BERTAC(ALBERT-xxlarge) represent BERTAC using RoBERTa-large and ALBERT-xxlarge as their base TLM, respectively. BERTAC with any of the base TLMs showed better EM (Exact match with the gold standard answers) than the state-of-the-art method, CFormer(RoBERTa-large), for both benchmarks (Quasar-T and SearchQA).

Citation

If you use this source code, we would appreciate if you cite the following paper:

@inproceedings{ohetal2021bertac,
  title={BERTAC: Enhancing Transformer-based Language Models 
         with Adversarially Pretrained Convolutional Neural Networks},
  author={Jong-Hoon Oh and Ryu Iida and 
          Julien Kloetzer and Kentaro Torisawa},
  booktitle={The Joint Conference of the 59th Annual Meeting  
             of the Association for Computational Linguistics  
             and the 11th International Joint Conference 
             on Natural Language Processing (ACL-IJCNLP 2021)},
  year={2021}
}

Acknowledgements

Part of the source codes is borrowed from HuggingFace Transformers v2.4.1 licensed under Apache 2.0, DrQA licensed under BSD, and Open-QA licensed under MIT.