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This is the repo of our EMNLP'20paper:

Scalable Multi-Hop Relational Reasoning for Knowledge-Aware Question AnsweringYanlin 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

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 downloadhere.

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

• 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 ipythonsource activate krqaconda install pytorch=1.1.0 torchvision cudatoolkit=10.0 -c pytorchpip install dgl-cu100==0.3.1pip install transformers==2.0.0 tqdm networkx==2.3 nltk spacy==2.1.6python -m spacy download en

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

git clone https://github.com/INK-USC/MHGRN.gitcd MHGRNbash scripts/download.sh

The script will:

• Download theCommonsenseQA dataset
• DownloadConceptNet
• Download pretrained TransE embeddings

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 indata/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)        ├── ...

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

Each graph encoding model is implemented in a single script:

Some important command line arguments are listed as follows (runpython {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

Toreproduce the reported results of MultiGRN on CommonsenseQA official set:

bash scripts/run_grn_csqa.sh

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 --modeeval [ --save_dir path/to/directory/ ]

• Convert your dataset to{train,dev,test}.statement.jsonl in .jsonl format (seedata/csqa/statement/train.statement.jsonl)
• Create a directory indata/{yourdataset}/ to store the .jsonl files
• Modifypreprocess.py and perform subgraph extraction for your data
• Modifyutils/parser_utils.py to support your own dataset
• Tuneencoder_lr,decoder_lr and other important hyperparameters, modifyutils/parser_utils.py and{model}.py to record the tuned hyperparameters