FAEA-FSRC

The source code of paper 《Function-words Adaptively Enhanced Attention Networks for Few-Shot Inverse Relation Classification》, accepted to IJCAI 2022.

Requirements

• python 3.6
• PyTorch 1.7.0
• transformers 4.0.0
• numpy 1.19

Datasets

We experiment our model on two few-shot relation extraction datasets,

1. FewRel 1.0
2. FewRel 2.0

Please download data from the official links and put it under the ./data/.

Evaluation

Please download trained model from here [usoa] and put it under the ./checkpoint/. To evaluate our model, use command

FewRel 1.0

Under 10-way-1-shot setting

python train.py \
    --N 10 --K 1 --Q 1 --test_iter 10000\
    --only_test True --load_ckpt "./checkpoint/FAEA-bert-train_wiki-val_wiki-10-1.pth.tar"

Under 5-way-1-shot setting

python train.py \
    --N 5 --K 1 --Q 1 --test_iter 10000\
    --only_test True --load_ckpt "./checkpoint/FAEA-bert-train_wiki-val_wiki-5-1.pth.tar"

Training

FewRel 1.0

To run our model, use command

python train.py

This will start the training and evaluating process of FAEA in a 10-way-1-shot setting. You can also use different args to start different process. Some of them are here:

• train / val / test: Specify the training / validation / test set.
• trainN: N in N-way K-shot. trainN is the specific N in training process.
• N: N in N-way K-shot.
• K: K in N-way K-shot.
• Q: Sample Q query instances for each relation.

There are also many args for training (like batch_size and lr) and you can find more details in our codes.

FewRel 2.0

Use command

python train.py \
    --val val_pubmed --test val_pubmed --ispubmed True 

Results

FewRel 1.0

	5-way-1-shot	5-way-5-shot	10-way-1-shot	10-way-5-shot
Val	90.81	94.24	84.22	88.74
Test	95.10	96.48	90.12	92.72