Source code for EMNLP2020 paper: Adaptive Attentional Network for Few-Shot Knowledge Graph Completion.
Few-shot Knowledge Graph (KG) completion is a focus of current research, where each task aims at querying unseen facts of a relation given few-shot reference entity pairs. This work proposes an adaptive attentional network for few-shot KG completion by learning adaptive entity and reference representations. Evaluation in link prediction on two public datasets shows that our approach achieves new state-of-the-art results with different few-shot sizes.
python 3.6
Pytorch == 1.1.0
CUDA: 9.0
GPU: Tesla T4
We adopt Nell and Wiki datasets to evaluate our model, FAAN. The orginal datasets and pretrain embeddings are provided from xiong's repo. For convenience, the datasets can be downloaded from Nell data and Wiki data. The pre-trained embeddings can be downloaded from Nell embeddings and Wiki embeddings. Note that all these files were provided by xiong and we just select what we need here. All the dataset files and the pre-trained TransE embeddings should be put into the directory ./NELL and ./Wiki, respectively.
To achieve the best performance, pls train the models as follows:
python trainer.py --weight_decay 0.0 --prefix nell.5shot
python trainer.py --dataset wiki --embed_dim 50 --num_transformer_layers 4 --num_transformer_heads 8 --dropout_input 0.3 --dropout_layers 0.2 --lr 6e-5 --prefix wiki.5shot
To test the trained models, pls run as follows:
python trainer.py --weight_decay 0.0 --prefix nell.5shot_best --test
python trainer.py --dataset wiki --embed_dim 50 --num_transformer_layers 4 --num_transformer_heads 8 --dropout_input 0.3 --dropout_layers 0.2 --lr 6e-5 --prefix wiki.5shot --test
If you find this code useful, pls cite our work:
@inproceedings{Sheng2020:FAAN,
author = {Jiawei Sheng and
Shu Guo and
Zhenyu Chen and
Juwei Yue and
Lihong Wang and
Tingwen Liu and
Hongbo Xu},
title = {Adaptive Attentional Network for Few-Shot Knowledge Graph Completion},
booktitle = {Proceedings of the 2020 Conference on Empirical Methods in Natural
Language Processing, {EMNLP} 2020, Online, November 16-20, 2020},
pages = {1681--1691},
publisher = {Association for Computational Linguistics},
year = {2020}
}
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