Relational Triple Extraction: One Step is Enough
Authors: Yu-Ming Shang, Heyan Huang, Xin Sun, Wei Wei, Xian-Ling Mao
IJCAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experimental results on two widely used datasets demonstrate that the proposed model performs better than the state-of-the-art baselines. |
| Researcher Affiliation | Academia | Yu-Ming Shang1 , Heyan Huang1 , Xin Sun1 , Wei Wei2 and Xian-Ling Mao1 1School of Computer Science & Technology, Beijing Institute of Technology, Beijing, China 2Huazhong University of Science and Technology, Hu bei, China |
| Pseudocode | No | The paper describes the proposed method in text and with a system architecture diagram (Figure 2), but does not contain structured pseudocode or an algorithm block. |
| Open Source Code | No | The paper does not include an explicit statement about releasing its own source code or a direct link to a code repository for the methodology described. |
| Open Datasets | Yes | We conduct experiments on two widely used relational triple extraction benchmarks: NYT [Riedel et al., 2010] and Web NLG [Gardent et al., 2017]. |
| Dataset Splits | Yes | Table 1: Statistics of datasets. ... NYT 56,195 4,999 5,000 ... Web NLG 5,019 500 703 |
| Hardware Specification | Yes | All experiments are conducted with a RTX 3090 GPU. |
| Software Dependencies | No | The paper mentions using a specific BERT model ('cased base version of BERT') but does not provide version numbers for general software dependencies such as programming languages or deep learning frameworks. |
| Experiment Setup | Yes | The dimension of token representation hi is d = 768. The dimension of projected entity representations de is set to 900. During training, the learning rate is 1e-5, and the batch size is set to 8 on NYT and NYT, 6 on Web NLG and Web NLG. The max length of candidate entities C is 9/6/12/21 on NYT /Web NLG /NYT/Web NLG respectively. For each sentence, we randomly select nneg = 100 negative entities from E to optimize the objective function of a minibatch. During inference, we predict links for all candidate entities and the max length C is 7/6/11/20 on NYT /Web NLG /NYT/Web NLG respectively. |