Awesome
Hybrid Attention-Based Prototypical Networks for Noisy Few-Shot Relation Classification
Code and data for AAAI2019 paper Hybrid Attention-Based Prototypical Networks for Noisy Few-Shot Relation Classification.
Author: Tianyu Gao*, Xu Han*, Zhiyuan Liu, Maosong Sun. (* means equal contribution)
Dataset and Word Embedding
We evaluate our models on FewRel, a large-scale dataset for few-shot relation classification. It has 100 relations and 700 instances for each relation. You can find some baseline models from here.
Due to the large size, we did not upload the glove file (pre-trained word embedding). Please download glove.6B.50d.json
from Tsinghua Cloud or Google Drive and put it under data/
folder.
Usage
To run our code, use this command for training
python train.py {MODEL_NAME} {N} {K} {NOISE_RATE}
and use this command for testing
python test.py {MODEL_NAME} {N} {K} {NOISE_RATE}
where {MODEL_NAME} could be proto
or proto_hatt
, {N}
is the num of classes, {K}
is the num of instances for each class and {NOISE_RATE}
is the probability that one instance is wrong-labeled.