MVD: Towards better Entity Linking with Multi-View enhanced Distillation

This repo provides the code for our ACL 2023 paper: Towards better Entity Linking with Multi-View Enhanced Distillation

Setup

Run the following command to install the required packages:

pip install -r requirements.txt

The code for our paper was run using 4 NVIDIA RTX A6000

Data Download & Preprocess

For ZESEHL dataset:

sh scripts/get_zeshel_data.sh

python utils/preprocess_data.py --dataset zeshel --mention_path data/zeshel/mentions --document_path data/zeshel/documents --output_path data/zeshel --max_seq_length 128 --max_ent_length 512 --max_view_length 40

For Wikipedia-based datasets:

wget http://dl.fbaipublicfiles.com/KILT/kilt_knowledgesource.json

sh scripts/get_wikipedia_data.sh

python utils/preprocess_data.py --dataset wikipedia \
--mention_path data/wikipedia/basic_data/test_datasets \
--document_path data/wikipedia/kilt_knowledgesource.json \
--output_path data/wikipedia/entity.json \
--max_seq_length 32 --max_ent_length 512 --max_view_length 40

Warmup Training: Retriever

For ZESEHL dataset:

Run the follow script to train the retriver or you can download the warmup-trained retriever here

sh scripts/run_retriever.sh

Computational cost:

Time:  around 0.6h,  1min per epoch

Memory: 20G per GPU

For Wikipedia-based Datasets: change several hyperparameters

--kb wikipedia 
--data_dir data/wikipedia
--entity_data_dir data/wikipedia/entity.json
--output_dir output/wikipedia
--bert_model bert-large-uncased
--max_seq_length 32
--max_view_num 5 

Generate Hard Negatives

Run the follow script to generate static hard negatives for the teacher model:

sh scripts/run_predict.sh

Warmup Training: Teacher

Run the follow script to train the teacher or you can download the warmup-trained teacher here

sh scripts/run_teacher.sh

Computational cost:

Time:  around 2h,  40min per epoch

Memory: 25G per GPU

MVD Training

Run the follow script to MVD or you can download the retriever after MVD training here

sh scripts/run_mvd.sh

Computational cost:

Time:   around 13h,  2.5h per epoch

Memory: 35G per GPU

Evaluation

Run the follow script to evaluate the retrieval performance:

sh scripts/run_evaluate.sh

Retrieval results on ZESHEL dataset

Model	Recall@1	Recall@2	Recall@4	Recall@8	Recall@16	Recall@32	Recall@50	Recall@64
Warmup	41.99	57.54	68.06	75.28	81.04	85.74	88.20	89.23
MVD (w/o global-view)	51.27	63.30	71.81	78.04	82.99	86.81	89.05	90.25
MVD	52.51	64.77	73.43	79.74	84.35	88.17	90.43	91.55

Retrieval results on Wikipedia-based datasets

Recall@1 of MVD is even higher than overall accuracy (retrieval-then-rank) of BLINK

AIDA-testb

Model	Recall@1	Recall@10	Recall@30	Recall@100	Overall Acc
BLINK	79.51	92.38	94.87	96.63	80.27
MuVER	-	94.53	95.25	98.11	-
MVD (w/o global-view)	84.32	97.05	98.15	98.80	-

MSNBC

Model	Recall@1	Recall@10	Recall@30	Recall@100	Overall Acc
BLINK	84.28	93.03	95.46	96.76	85.09
MuVER	-	95.02	96.62	97.75	-
MVD (w/o global-view)	85.66	96.74	97.71	98.04	-

WNED-CWEB

Model	Recall@1	Recall@10	Recall@30	Recall@100	Overall Acc
BLINK	67.07	82.23	86.09	88.68	68.28
MuVER	-	79.31	83.94	88.15	-
MVD (w/o global-view)	68.87	85.01	88.18	91.11	-

Acknowledgements

We thank BLINK and MuVER for the base infrastructure of this project.