Code for our paper "Schema-aware Reference as Prompt Improves Data-Efficient Relational Triple and Event Extraction".

Java 8 # for elasticsearch
elasticsearch==7.17.1

• This requirement is for retrieving tools.

For different base models, you can generate the reference by following codes:

cd retrieval/ 
python retrieve.py --base_model prgc

The parameter --base_model is for different base models, we can change it in prgc, relationprompt, t2e, degree.

For Text2Event and DEGREE, please follow the instruction README.md document in their corresponding folder to preprocess the datasets, and then generate the retrieved reference.

We plugged RAP to several base models, which can be seen in the folders below:

BaseModel
├── DEGREE
├── PRGC
├── RelationPrompt
└── Text2Event

The code of above base models are borrowed from their original codes with slight modifacations.

DEGREE : Please follow the instruction here.

PRGC : Please follow the instruction here.

RelationPrompt : Please follow the instruction here.

Text2Event : Please follow the instruction here.

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

@article{DBLP:journals/corr/abs-2210-10709,
  author    = {Yunzhi Yao and
               Shengyu Mao and
               Xiang Chen and
               Ningyu Zhang and
               Shumin Deng and
               Huajun Chen},
  title     = {Schema-aware Reference as Prompt Improves Data-Efficient Relational
               Triple and Event Extraction},
  journal   = {CoRR},
  volume    = {abs/2210.10709},
  year      = {2022},
  url       = {https://doi.org/10.48550/arXiv.2210.10709},
  doi       = {10.48550/arXiv.2210.10709},
  eprinttype = {arXiv},
  eprint    = {2210.10709},
  timestamp = {Tue, 25 Oct 2022 14:25:08 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2210-10709.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}