Sequence Generation with Label Augmentation for Relation Extraction
Authors: Bo Li, Dingyao Yu, Wei Ye, Jinglei Zhang, Shikun Zhang
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that RELA achieves competitive results compared with previous methods on four RE datasets. |
| Researcher Affiliation | Academia | 1National Engineering Research Center for Software Engineering, Peking University 2School of Software and Microelectronics, Peking University deepblue.lb@stu.pku.edu.cn, yudingyao@pku.edu.cn, wye@pku.edu.cn, jinglei.zhang@stu.pku.edu.cn, zhangsk@pku.edu.cn |
| Pseudocode | No | The paper describes its methods in prose and with supporting diagrams (e.g., Figure 2) but does not include any explicitly labeled 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | Yes | Code is available at https://github.com/pkuserc/RELA |
| Open Datasets | Yes | We evaluate our method on four RE datasets, i.e., TACRED (Zhang et al. 2017), Sem Eval2010 (Hendrickx et al. 2010), Google RE,7 and sci ERC (Luan et al. 2018). |
| Dataset Splits | Yes | Dataset #Train #Dev #Test #Class TACRED 68,124 22,613 15,509 42 Sem Eval 8,000 2,712 10 Google RE 38,112 9,648 9,616 5 sci ERC 3,219 455 974 7 |
| Hardware Specification | Yes | We use Pytorch (Paszke et al. 2019) and Tesla T4 GPU with a batch size of 8. |
| Software Dependencies | No | The paper mentions software like 'Pytorch' and 'BART-large' and 'GPT-2' but does not provide specific version numbers for these software components. For example, it states 'We use Pytorch (Paszke et al. 2019)' but doesn't specify the PyTorch version number. |
| Experiment Setup | Yes | The maximum input/output sequence length is 256/32 tokens. As an optimiser, we used AdamW (Loshchilov and Hutter 2019) with a 1e-5 learning rate and a 0.2 warmup ratio. The training epoch is 20 for Semeval and 10 for other datasets. |