Curriculum-Meta Learning for Order-Robust Continual Relation Extraction
Authors: Tongtong Wu, Xuekai Li, Yuan-Fang Li, Gholamreza Haffari, Guilin Qi, Yujin Zhu, Guoqiang Xu10363-10369
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our comprehensive experiments on three benchmark datasets show that our proposed method outperforms the state-of-the-art techniques. |
| Researcher Affiliation | Collaboration | 1School of Computer Science and Engineering, Southeast University, Nanjing, China 2Faculty of Information Technology, Monash University, Melbourne, Australia 3Gamma Lab, Ping An One Connect, Shanghai, China |
| Pseudocode | Yes | Algorithm 1: Curriculum-Meta Learning |
| Open Source Code | Yes | The code is available at https://github.com/wutong8023/AAAI-CML. |
| Open Datasets | Yes | We conduct our experiments on three datasets, including Continual-Few Rel, Continual-Simple Questions, and Continual-TACRED, which were introduced in (?). |
| Dataset Splits | No | The paper mentions forming training and testing sets but does not specify a separate validation split or its details. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory, or cloud instance types) used for running experiments are mentioned in the paper. |
| Software Dependencies | No | The paper mentions the Adam optimizer but does not provide specific version numbers for any software dependencies or libraries. |
| Experiment Setup | No | The paper states 'see Appendix B for hyperparameters', indicating that specific experimental setup details are not provided in the main text. |