Document-level Relation Extraction as Semantic Segmentation
Authors: Ningyu Zhang, Xiang Chen, Xin Xie, Shumin Deng, Chuanqi Tan, Mosha Chen, Fei Huang, Luo Si, Huajun Chen
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that our approach can obtain state-of-the-art performance on three benchmark datasets Doc RED, CDR, and GDA1. 4 Experiments |
| Researcher Affiliation | Collaboration | Ningyu Zhang1,2 , Xiang Chen 1,2 , Xin Xie1,2 , Shumin Deng1,2 , Chuanqi Tan3 , Mosha Chen3 , Fei Huang3 , Luo Si3 , Huajun Chen1,2 1 Zhejiang University & AZFT Joint Lab for Knowledge Engine 2 Hangzhou Innovation Center, Zhejiang University 3 Alibaba Group {zhangningyu,xiang chen,xx2020,231sm,huajunsir}@zju.edu.cn {chuanqi.tcq,chenmosha.cms,f.huang,luo.si}@alibaba-inc.com |
| Pseudocode | No | The paper describes the methodology in text but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | 1The code and datasets are available in https://github.com/zjunlp/ Docu Net. |
| Open Datasets | Yes | We evaluated our Docu Net model on three document-level RE datasets. ... Doc RED [Yao et al., 2019] is a large-scale documentlevel relation extraction dataset by crowdsourcing. ... CDR [Li et al., 2016] is a relation extraction dataset in the biomedical domain... GDA [Wu et al., 2019] is a dataset in the biomedical domain... |
| Dataset Splits | Yes | Doc RED contains 3,053/1,000/1,000 instances for training, validating and test, respectively. We listed the dataset statistics in Table 1. |
| Hardware Specification | Yes | We trained on one NVIDIA V100 16GB GPU and evaluated our model with Ign F1, and F1 following [Yao et al., 2019]. |
| Software Dependencies | No | Our model was implemented based on Pytorch. We used cased BERT-base, or Ro BERTa-large as the encoder on Doc RED and Sci BERT-base [Beltagy et al., 2019] on CDR and GDA. We optimize our model with Adam W using learning rates 2e 5 with a linear warmup for the first 6% of steps. |
| Experiment Setup | Yes | We optimize our model with Adam W using learning rates 2e 5 with a linear warmup for the first 6% of steps. We set the matrix size N = 42. The context-based strategy is utilized by default. |