Cold-start Multi-hop Reasoning by Hierarchical Guidance and Self-verification

This is the PyTorch implementation of of the SelfHier model for multi-hop reasoning.

Overview

We present SelfHier, which utilizes the Self-verification and Hierarchical Guidance to conduct multi-hop reasoning. Here is an overview of our model architecture:

Training SelfHier

To reproduce our results or extend SelfHier model to more datasets, we provide a detailed description as follow:

1、Go data/ folder, generate mapping files by running:

python utils.py --dataset NELL23K --gen-mapping --gen-eval-data

2、Then, our model utilizes AnyBURL to mine logical rules.

We provide a convenient script run.sh under AnyBURL/ for mining and filtering high confidence rules (please modify the dataset name in run.sh and config-learn.properties). The above step helps generate rule.dict containing high quality rules under the dataset folder, then go to data/ folder and run:

python utils.py --dataset NELL23K --gen-train-data --num 6 --out 6_rev_rule --max-len 3 --rule

3、Go data/NELL23K/ folder, generate the type_constrain.txt

python n-n.py

4、Go OpenKE-OpenKE-PyTorch/openke folder

bash make.sh

5、Go OpenKE-OpenKE-PyTorch folder, training embedding

python train_transe_NELL23K.py

6、Go SelfHier-main/ folder

python process_hierarchical_guidance.py --datapath 'data/NELL23K/'

7、Train SlefHier

python train.py --dataset NELL23K --batch-size 1024 --lr 5e-4 --dropout 0.1 --eval-begin 50 --num-epoch 50 --save-interval 50 --save-dir "model_NELL23K" --label-smooth 0.25 --trainset "6_rev_rule" --prob 0.15 --beam-size 512 --test-batch-size 4

Other training Setting

python train.py --dataset NELL995 --batch-size 512 --lr 1e-3 --dropout 0.1 --eval-begin 25 --num-epoch 25 --save-interval 25 --save-dir "model_NELL995" --label-smooth 0.25 --trainset "6_rev_rule" --prob 0.15 --beam-size 512 --test-batch-size 2 --warmup 10
python train.py --dataset FB15K237 --batch-size 1024 --lr 5e-4 --dropout 0.1 --eval-begin 50 --num-epoch 50 --save-interval 50 --save-dir "model_FB15K237" --label-smooth 0.25 --trainset "6_rev_rule" --prob 0.15 --beam-size 512 --test-batch-size 8 --warmup 3
python train.py --dataset FB15K237-20 --batch-size 1024 --lr 1e-4 --dropout 0.1 --eval-begin 15 --num-epoch 15 --save-interval 15 --save-dir "model_FB15K237-20" --label-smooth 0.25 --trainset "6_rev_rule" --prob 0.25 --beam-size 512 --test-batch-size 4
python train.py --dataset NELL23K-40 --batch-size 1024 --lr 5e-4 --dropout 0.1 --eval-begin 5 --num-epoch 5 --save-interval 5 --save-dir "model_NELL23K-40" --label-smooth 0.25 --trainset "6_rev_rule" --prob 0.15 --beam-size 512 --test-batch-size 4
python train.py --dataset NELL23K-50 --batch-size 1024 --lr 5e-4 --dropout 0.1 --eval-begin 5 --num-epoch 5 --save-interval 5 --save-dir "model_NELL23K-50" --label-smooth 0.25 --trainset "6_rev_rule" --prob 0.15 --beam-size 512 --test-batch-size 4
python train.py --dataset NELL23K-60 --batch-size 1024 --lr 5e-4 --dropout 0.1 --eval-begin 5 --num-epoch 5 --save-interval 5 --save-dir "model_NELL23K-60" --label-smooth 0.25 --trainset "6_rev_rule" --prob 0.15 --beam-size 512 --test-batch-size 4
python train.py --dataset FB15K23720-10 --batch-size 1024 --lr 1e-4 --dropout 0.1 --eval-begin 10 --num-epoch 10 --save-interval 10 --save-dir "model_FB15K23720-10" --label-smooth 0.25 --trainset "6_rev_rule" --prob 0.25 --beam-size 512 --test-batch-size 4
python train.py --dataset FB15K23720-20 --batch-size 1024 --lr 1e-4 --dropout 0.1 --eval-begin 10 --num-epoch 10 --save-interval 10 --save-dir "model_FB15K23720-20" --label-smooth 0.25 --trainset "6_rev_rule" --prob 0.25 --beam-size 512 --test-batch-size 4
python train.py --dataset FB15K23720-50 --batch-size 1024 --lr 1e-4 --dropout 0.1 --eval-begin 10 --num-epoch 10 --save-interval 10 --save-dir "model_FB15K23720-50" --label-smooth 0.25 --trainset "6_rev_rule" --prob 0.25 --beam-size 512 --test-batch-size 4

Note that the train.txt in each dataset is pre-complete by Self-verification on raw data train_raw.txt.

If you want to leveraging the Self-verification to pre-complete the data by yourself, rmoving the train.txt and renameing the train_raw.txt to train.txt. Then, runing the STEP1 to STEP6 and:

python train.py --dataset NELL23K --eval-pattern 1 --batch-size 1024 --lr 5e-4 --dropout 0.1 --eval-begin 50 --num-epoch 50 --save-interval 50 --save-dir "model_NELL23K" --label-smooth 0.25 --trainset "6_rev_rule" --prob 0.15 --beam-size 512 --test-batch-size 4

After getting the new_dict.json, run:

python process_self_verification.py --datapath 'data/NELL23K'

Rename the train_new.txt to train.txt and run the STEP1 to STEP7 again.