Learning Contextualized Knowledge Structures for Commonsense Reasoning

This repo contains the code for paper Learning Contextualized Knowledge Structures for Commonsense Reasoning, accepted to Findings of ACL-IJCNLP 2021.

The code is based on MHGRN. We thank the authors for open-sourcing their code.

Requirement

• A new conda environment

conda create -n HGN python=3.7

conda activate HGN

• PyTorch 1.6.0 + CUDA 10.1

conda install pytorch==1.6.0 cudatoolkit=10.1 -c pytorch

• PyTorch Geometric

pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-1.6.0+cu101.html

pip install torch-sparse -f https://pytorch-geometric.com/whl/torch-1.6.0+cu101.html

pip install torch-cluster -f https://pytorch-geometric.com/whl/torch-1.6.0+cu101.html

pip install torch-spline-conv -f https://pytorch-geometric.com/whl/torch-1.6.0+cu101.html

pip install torch-geometric

• Transformers 3.1.0, spaCy 2.3.5, ConfigArgParse, NLTK

pip install transformers==3.1.0 spacy==2.3.5 ConfigArgParse nltk

python -m spacy download en

Data Preparation

git clone https://github.com/INK-USC/HGN.git
cd HGN
bash download.sh

The script will:

• download preprocessed ConceptNet with pretrained entity and relation embeddings;
• download CommonsenseQA dataset;
• create folders for storing the preprocessed dataset.

Edge Generator

1. Build the training/dev data based on ConceptNet facts.

   python edge_generator_preprocess.py

2. Train a generator.

   python edge_generator_train.py --config config/edge_generator_train.yaml

HGN

1. Preprocess the dataset.

   python preprocess.py

   The script will generate for each QA pair:

   • grounded question and answer concepts;
   • an extracted KG subgraph;
   • a jsonl that stores the adj and non-adj concept pairs;
   • a hybrid graph structure (.pk) without generated features.

2. Generate features (.pt) for non-adj concept pairs with the generator.

   It takes the non-adj concept pairs for each instance as input and generate features (.pt) with a jsonl as an intermediate output.

   python generate_edge.py --batch_size 100 \
       --input_non_adj_pairs_jsonl ./data/csqa/hybrid/train_cpt_pairs_1hop_hybrid.jsonl \
       --output_gen_rel_jsonl ./data/csqa/hybrid/relgen/train_cpt_pairs_1hop_hybrid.gen.jsonl \
       --output_pt ./data/csqa/hybrid/relgen/train_cpt_pairs_1hop_hybrid.jsonl.pt
   python generate_edge.py --batch_size 100 \
       --input_non_adj_pairs_jsonl ./data/csqa/hybrid/dev_cpt_pairs_1hop_hybrid.jsonl \
       --output_gen_rel_jsonl ./data/csqa/hybrid/relgen/dev_cpt_pairs_1hop_hybrid.gen.jsonl \
       --output_pt ./data/csqa/hybrid/relgen/dev_cpt_pairs_1hop_hybrid.jsonl.pt
   python generate_edge.py --batch_size 100 \
       --input_non_adj_pairs_jsonl ./data/csqa/hybrid/test_cpt_pairs_1hop_hybrid.jsonl \
       --output_gen_rel_jsonl ./data/csqa/hybrid/relgen/test_cpt_pairs_1hop_hybrid.gen.jsonl \
       --output_pt ./data/csqa/hybrid/relgen/test_cpt_pairs_1hop_hybrid.jsonl.pt

3. Train a HGN model with RoBERTa-large as the text encoder.

   python hgn.py --config config/csqa_roberta.yaml

   Hyperparameters are specified in config/csqa_roberta.yaml. The final checkpoint will be saved under save_dir. Dev and test accuracy will be printed.

4. (Optional) Output predictions on the test set using a trained HGN model.

   python hgn.py --config config/csqa_roberta.yaml --mode pred \
       --test_model_path <PATH_TO_CHECKPOINT_PT> \
       --output_pred_path <PATH_TO_PREDICTION>

Citation

@inproceedings{yan2021learning,
 address = {Online},
 author = {Yan, Jun and Raman, Mrigank and Chan, Aaron and Zhang, Tianyu and Rossi, Ryan  and Zhao, Handong and Kim, Sungchul and Lipka, Nedim and Ren, Xiang},
 booktitle = {Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021},
 doi = {10.18653/v1/2021.findings-acl.354},
 pages = {4038--4051},
 publisher = {Association for Computational Linguistics},
 title = {Learning Contextualized Knowledge Structures for Commonsense Reasoning},
 url = {https://aclanthology.org/2021.findings-acl.354},
 year = {2021}
}