Multi-Hop Graph Relation Networks (EMNLP 2020)

This is the repo of our EMNLP'20 paper:

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.
*=equal contritbution

This repository also implements other graph encoding models for question answering (including vanilla LM finetuning).

• RelationNet
• R-GCN
• KagNet
• GConAttn
• KVMem
• MHGRN (or. MultiGRN)

Each model supports the following text encoders:

• LSTM
• GPT
• BERT
• XLNet
• RoBERTa

Resources

We provide preprocessed ConceptNet and pretrained entity embeddings for your own usage. These resources are independent of the source code.

Note that the following reousrces can be download here.

ConceptNet (5.6.0)

Entity Embeddings (Node Features)

Entity embeddings are packed into a matrix of shape (#ent, dim) and stored in numpy format. Use np.load to read the file. You may need to download the vocabulary files first.

Dependencies

• Python >= 3.6
• PyTorch == 1.1.0
• transformers == 2.0.0
• tqdm
• dgl == 0.3.1 (GPU version)
• networkx == 2.3

Run the following commands to create a conda environment (assume CUDA10):

conda create -n krqa python=3.6 numpy matplotlib ipython
source activate krqa
conda install pytorch=1.1.0 torchvision cudatoolkit=10.0 -c pytorch
pip install dgl-cu100==0.3.1
pip install transformers==2.0.0 tqdm networkx==2.3 nltk spacy==2.1.6
python -m spacy download en

Usage

1. Download Data

First, you need to download all the necessary data in order to train the model:

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

The script will:

• Download the CommonsenseQA dataset
• Download ConceptNet
• Download pretrained TransE embeddings

2. Preprocess

To preprocess the data, run:

python preprocess.py

By default, all available CPU cores will be used for multi-processing in order to speed up the process. Alternatively, you can use "-p" to specify the number of processes to use:

python preprocess.py -p 20

The script will:

• Convert the original datasets into .jsonl files (stored in data/csqa/statement/)
• Extract English relations from ConceptNet, merge the original 42 relation types into 17 types
• Identify all mentioned concepts in the questions and answers
• Extract subgraphs for each q-a pair

The preprocessing procedure takes approximately 3 hours on a 40-core CPU server. Most intermediate files are in .jsonl or .pk format and stored in various folders. The resulting file structure will look like:

.
├── README.md
└── data/
    ├── cpnet/                 (prerocessed ConceptNet)
    ├── glove/                 (pretrained GloVe embeddings)
    ├── transe/                (pretrained TransE embeddings)
    └── csqa/
        ├── train_rand_split.jsonl
        ├── dev_rand_split.jsonl
        ├── test_rand_split_no_answers.jsonl
        ├── statement/             (converted statements)
        ├── grounded/              (grounded entities)
        ├── paths/                 (unpruned/pruned paths)
        ├── graphs/                (extracted subgraphs)
        ├── ...

3. Hyperparameter Search (optional)

To search the parameters for RoBERTa-Large on CommonsenseQA:

bash scripts/param_search_lm.sh csqa roberta-large

To search the parameters for BERT+RelationNet on CommonsenseQA:

bash scripts/param_search_rn.sh csqa bert-large-uncased

4. Training

Each graph encoding model is implemented in a single script:

Some important command line arguments are listed as follows (run python {lm,rn,rgcn,...}.py -h for a complete list):

For example, run the following command to train a RoBERTa-Large model on CommonsenseQA:

python lm.py --encoder roberta-large --dataset csqa

To train a RelationNet with BERT-Large-Uncased as the encoder:

python rn.py --encoder bert-large-uncased

To reproduce the reported results of MultiGRN on CommonsenseQA official set:

bash scripts/run_grn_csqa.sh

5. Evaluation

To evaluate a trained model (you need to specify --save_dir if the checkpoint is not stored in the default directory):

python {lm,rn,rgcn,...}.py --mode eval [ --save_dir path/to/directory/ ]

Use Your Own Dataset

• Convert your dataset to {train,dev,test}.statement.jsonl in .jsonl format (see data/csqa/statement/train.statement.jsonl)
• Create a directory in data/{yourdataset}/ to store the .jsonl files
• Modify preprocess.py and perform subgraph extraction for your data
• Modify utils/parser_utils.py to support your own dataset
• Tune encoder_lr,decoder_lr and other important hyperparameters, modify utils/parser_utils.py and {model}.py to record the tuned hyperparameters