FAAN

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.

Requirements

python 3.6
Pytorch == 1.1.0
CUDA: 9.0
GPU: Tesla T4

Datasets

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.

How to run

To achieve the best performance, pls train the models as follows:

Nell

python trainer.py --weight_decay 0.0 --prefix nell.5shot

Wiki

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:

Nell

python trainer.py --weight_decay 0.0 --prefix nell.5shot_best --test

Wiki

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

Citation

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}
}