NLPlab-skku / MTL-KGC

code for the paper 'Multi-Task Learning for Knowledge Graph Completion with Pre-trained Language Models'

MTL-KGC

This is PyTorch implementation of the Multi-Task Learning for Knowledge Graph Completion with Pre-trained Language Models.

Train

Train multitask learning with link prediction (LP), relation prediction (RP), and relevance ranking (RR).

python run_bert_multitask.py \
    --do_train \
    --task_list lp,rp,rr \
    --data_dir ./data/wn18rr \
    --bert_model bert-base-cased \
    --max_seq_length 128 \
    --output_dir ./output_dir \
    --num_train_epochs 5.0 \
    --learning_rate=2e-5 \
    --tb_log_dir=runs/log_dir

Train link prediction only

python run_bert_multitask.py \
    --do_train \
    --task_list lp \
    --data_dir ./data/wn18rr \
    --bert_model bert-base-cased \
    --max_seq_length 128 \
    --output_dir ./output_dir \
    --num_train_epochs 5.0 \
    --learning_rate=2e-5 \
    --tb_log_dir=runs/log_dir

Evaulation

python run_bert_multitask.py \
    --do_eval \
    --data_dir ./data/wn18rr \
    --bert_model {test_model} \
    --max_seq_length 128 \
    --output_dir ./output_dir \

Performance

WN18RR

	MRR	MR	HITS@1	HITS@3	HITS@10
LP (Yao et al, 2019)	0.219	108	0.095	0.243	0.497
LP + RP	0.302	112	0.177	0.353	0.560
LP + RR	0.277	97	0.130	0.341	0.576
LP + RP + RR	0.331	89	0.203	0.383	0.597

FB15k-237

	MRR	MR	HITS@1	HITS@3	HITS@10
LP (Yao et al, 2019)	0.237	145	0.144	0.260	0.427
LP + RP	0.262	138	0.169	0.289	0.447
LP + RR	0.247	143	0.154	0.272	0.434
LP + RP + RR	0.267	132	0.172	0.298	0.458

Citation

If you use the codes, please cite the following paper:

@inproceedings{kim2020multi,
  title={Multi-Task Learning for Knowledge Graph Completion with Pre-trained Language Models},
  author={Kim, Bosung and Hong, Taesuk and Ko, Youngjoong and Seo, Jungyun},
  booktitle={Proceedings of the 28th International Conference on Computational Linguistics},
  pages={1737--1743},
  year={2020}
}