Online Noisy Continual Relation Learning
Authors: Guozheng Li, Peng Wang, Qiqing Luo, Yanhe Liu, Wenjun Ke
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on Few Rel, TACRED and NYT-H with real-world noise demonstrate that our framework greatly outperforms the combinations of the state-of-the-art online continual learning and noisy label learning methods. |
| Researcher Affiliation | Academia | Guozheng Li1, Peng Wang1*, Qiqing Luo1, Yanhe Liu1, Wenjun Ke1,2 1School of Computer Science and Engineering, Southeast University 2Beijing Institute of Computer Technology and Application {gzli, pwang, qqluo, liuyanhe}@seu.edu.cn, kewenjun2191@163.com |
| Pseudocode | Yes | Algorithm 1: Online Purifying and Learning for S6 |
| Open Source Code | No | The paper does not contain any explicit statement about releasing source code or provide a link to a code repository. |
| Open Datasets | Yes | We test on two datasets of Few Rel (Han et al. 2018) and TACRED (Zhang et al. 2017) with synthetic noise. And we adopt the NYT-H (Zhu et al. 2020) dataset obtained by distant supervision (Mintz et al. 2009) to evaluate under the real-world noise. |
| Dataset Splits | No | The paper describes how tasks are created and mentions a test set for NYT-H, but does not explicitly specify train/validation/test dataset splits with percentages or counts for reproducibility, focusing instead on online streaming data and replay buffers. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU or CPU models, memory, or cloud instance types used for experiments. |
| Software Dependencies | No | The paper mentions using BERT as a backbone but does not provide specific version numbers for software dependencies like BERT, PyTorch/TensorFlow, Python, or other libraries. |
| Experiment Setup | Yes | The size of memory for replaying is set to 1,600, 800, 400 for Few Rel, TACRED and NYT-H, respectively. For noisy label learning methods, we use α = β = 1.0 in SL, α = 0.1, β = 0.4, λ = 600 in Pencil, and λ = 0.1 in Jo Co R, respectively. For fair comparison, We need to ensure that S6 uses roughly the same size of replay buffer as others. For Few Rel, we set the size of D, C, N as 1,000, 1,000 and 2,000. For TACRED and NYT-H, we set the size of D, C, N as 200, 200 and 400. The learning rates of fϕ( ) and fθ( ) are 5e-6 and 5e-5. The update sizes K1 and K2 are both 5. The batch size is 16 for both online and offline training. And we train 60 epochs in the finetuning stage. |