Few-shot Relation Extraction via Bayesian Meta-learning on Relation Graphs

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Deep Learning FewShotRE
Overview

Few-shot Relation Extraction via Bayesian Meta-learning on Relation Graphs

This is an implemetation of the paper Few-shot Relation Extraction via Bayesian Meta-learning on Relation Graphs.

Pretrain files

The codes rely on pre-trained BERT models. Please download pretrain.tar from Tsinghua Cloud and put it under the root. Then run tar xvf pretrain.tar to decompress it.

Usage

To run the model on the FewRel dataset, we could use the following command:

python train_demo.py --trainN 5 --N 5 --K 1 --Q 1 --model regrab --encoder bert --hidden_size 768 --val_step 1000 --batch_size 8 --fp16 --seed 1

Acknowledgement

Most of the codes are from the FewRel repo, which provides a neat codebase for few-shot relation extraction.

Citation

Please consider citing the following paper if you find our codes helpful. Thank you!

@inproceedings{qu2020few,
title={Few-shot Relation Extraction via Bayesian Meta-learning on Relation Graphs},
author={Qu, Meng and Gao, Tianyu and Xhonneux, Louis-Pascal AC and Tang, Jian},
booktitle={International Conference on Machine Learning},
year={2020}
}
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Comments
  • Questions about paper

    Questions about paper

    Hi, Huang! Glad to read your inspiring paper, and I have some questions about the paper details.

    1. The paper summarizes the graph meta learning problem into 3 categories, and declaims meta-gnn is not appliable for multiple graph cases. However, meta-gnn just uses maml on gcn or sgc, which doesn't limit its application ocassions. So I think you can extend the algorithm to multiple graph cases just by sampling tasks on different graphs and train together.
    2. Do you have comparing experiments on citeseer or cora datasets which are used more commonly? I see the meta-gnn result in table (3) is quite low which seems very strange comparing to original paper outstanding results. Thanks!
    opened by ligeng0197 0
  • how to get relation embeddings and relation graph?

    how to get relation embeddings and relation graph?

    Hi, in this repo, relation embeddings and relation graph are already provided. I wonder how do you use GCN to encode relations and their graph? Could you release the implementation of getting prior in the paper? Thanks!

    opened by ShellingFord221 0
Owner
MilaGraph
Research group led by Prof. Jian Tang at Mila-Quebec AI Institute (https://mila.quebec/) focusing on graph representation learning and graph neural networks.
MilaGraph
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