The repository contains code examples for DLG4NLP tutorials at NAACL 2021, SIGIR 2021, KDD 2021 and IJCAI 2021.

Slides can be downloaded from NAACL 2021 version, SIGIR 2021 version and KDD 2021 version.

You will need to install our graph4nlp library in order to run the demo code. Please follow the following environment setup instructions. Please also refer to the graph4nlp repository page for more details on how to use the library.

1. Create virtual environment

conda create --name graph4nlp python=3.7
conda activate graph4nlp

2. Install graph4nlp library

• Clone the github repo

git clone -b stable_202108 https://github.com/graph4ai/graph4nlp.git
cd graph4nlp

Please use stable instead of stable_202108 if you want to run the NAACL 2021 or SIGIR 2021 demos.

• Then run ./configure (or ./configure.bat if you are using Windows 10) to config your installation. The configuration program will ask you to specify your CUDA version. If you do not have a GPU, please choose 'cpu'.

./configure

• Finally, install the package

python setup.py install

3. Install other packages

pip install torchtext
pip install notebook

4. Set up StanfordCoreNLP (for static graph construction only, unnecessary for this demo because preprocessed data is provided)

• Download StanfordCoreNLP
• Go to the root folder and start the server

java -mx4g -cp "*" edu.stanford.nlp.pipeline.StanfordCoreNLPServer -port 9000 -timeout 15000

After complete the above steps, you can start the jupyter notebook server to run the demo:

cd graph4nlp_demo/XYZ
jupyter notebook

Note that you will need to change XYZ to the specific folder name.

• Graph4NLP library
• DLG4NLP website
• DLG4NLP survey
• DLG4NLP literature repo