Graph Soft Counter

This repo contains PyTorch implementation for paper: GNN is a Counter? Revisiting GNN for Question Answering (ICLR 2022).

@inproceedings{gsc,
author = {Wang, Kuan and Zhang, Yuyu and Yang, Diyi and Song, Le and Qin, Tao},
title = {GNN is a Counter? Revisiting GNN for Question Answering},
booktitle = {International conference on learning representations (ICLR), 2022},
year = {2022}
}

Environment

You can just run the bash file 'setup.sh' or run the following commands to create a conda environment:

conda create -n gsc python=3.9
source activate gsc
conda install pytorch torchvision torchaudio cudatoolkit=10.2 -c pytorch
conda install pyg -c pyg
conda install transformers

Dataset

We use the question answering datasets (CommonsenseQA, OpenBookQA) and the ConceptNet knowledge graph. We process the dataset following QA-GNN, and the preprocessing may take long. For your convenience, you can download all the processed data by

./download_preprocessed_data.sh

The resulting file structure will look like:

.
├── README.md
├── data/
    ├── cpnet/                 (prerocessed ConceptNet)
    ├── csqa/
        ├── train_rand_split.jsonl
        ├── dev_rand_split.jsonl
        ├── test_rand_split_no_answers.jsonl
        ├── statement/             (converted statements)
        ├── grounded/              (grounded entities)
        ├── graphs/                (extracted subgraphs)
        ├── ...
    └── obqa/

2. Train GSC

For CommonsenseQA, run

./run_gsc__csqa.sh

For OpenBookQA, run

./run_gsc__obqa.sh

As configured in these scripts, the model needs two types of input files

--{train,dev,test}_statements: preprocessed question statements in jsonl format. This is mainly loaded by load_input_tensors function in utils/data_utils.py.
--{train,dev,test}_adj: information of the KG subgraph extracted for each question. This is mainly loaded by load_sparse_adj_data_with_contextnode function in utils/data_utils.py. load_sparse_adj_data_with_contextnode Note: We find that training for OpenBookQA is unstable (e.g. best dev accuracy varies when using different seeds, different versions of the transformers / torch-geometric libraries, etc.), likely because the dataset is small. We suggest trying out different seeds. Another potential way to stabilize training is to initialize the model with one of the successful checkpoints provided below, e.g. by adding an argument --load_model_path obqa_model.pt.

3. Evaluate trained model

For CommonsenseQA, run

./eval_gsc__csqa.sh

For OpenBookQA, run

./eval_gsc__obqa.sh

Trained model examples

CommonsenseQA

Trained model	In-house Dev acc.	In-house Test acc.
RoBERTa-large + GSC [link]	0.7969	0.7478

OpenBookQA

Trained model	Dev acc.	Test acc.
RoBERTa-large + GSC [link]	0.6960	0.7060

Note: The models were trained and tested with HuggingFace transformers==2.0.1.

Acknowledgment

This repo is built upon the following works:

QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answering. Michihiro Yasunaga and Hongyu Ren and Antoine Bosselut and Percy Liang and Jure Leskovec. NAACL 2021. 
https://github.com/michiyasunaga/qagnn

Scalable Multi-Hop Relational Reasoning for Knowledge-Aware Question Answering. Yanlin Feng*, Xinyue Chen*, Bill Yuchen Lin, Peifeng Wang, Jun Yan and Xiang Ren. EMNLP 2020. 
https://github.com/INK-USC/MHGRN

Many thanks to the authors and developers!

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