Continual Relation Extraction via Sequential Multi-Task Learning
Authors: Thanh-Thien Le, Manh Nguyen, Tung Thanh Nguyen, Linh Ngo Van, Thien Huu Nguyen
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through extensive experiments on multiple datasets, CREST demonstrates significant improvements in CRE performance as well as superiority over other state-of-the-art Multi-task Learning frameworks... Experimental Results Datasets & Settings We evaluate our proposed method and all baselines on two English datasets: Few Rel (Han et al. 2018) dataset comprises 80 relation types and contains a total of 56,000 samples. ... TACRED (Zhang et al. 2017) dataset presents an imbalanced scenario for relation extraction (RE) with 42 relations, including the no relation class, and a total of 106,264 samples. |
| Researcher Affiliation | Collaboration | 1Vin AI Research, Vietnam 2Hanoi University of Science and Technology, Vietnam 3University of Michigan, USA 4University of Oregon, USA |
| Pseudocode | Yes | Algorithm 1: Adaptive Unified Gradient Descent for CRE |
| Open Source Code | No | The paper does not provide an explicit statement about the release of source code or a link to a code repository for the described methodology. |
| Open Datasets | Yes | We evaluate our proposed method and all baselines on two English datasets: Few Rel (Han et al. 2018) dataset comprises 80 relation types and contains a total of 56,000 samples. ... TACRED (Zhang et al. 2017) dataset presents an imbalanced scenario for relation extraction (RE) with 42 relations, including the no relation class, and a total of 106,264 samples. |
| Dataset Splits | Yes | In line with Wang et al. s paper (2019), this paper adopts the same configurations and utilizes the original training set and validation set as the foundation for conducting experiments. |
| Hardware Specification | Yes | Computing infrastructure: Single NVIDIA A100 40GB. |
| Software Dependencies | Yes | PyTorch 2.0.0+cu117 and Huggingface Transformer 4.33.0 are used to implement the models. |
| Experiment Setup | Yes | Batch size: 16, similar to CRL (Zhao et al. 2022) 1Learning rate: {10 5, 2 10 5, 10 4} 1Number of embeddings training epoch: {10, 20, 50} 1Number of classifier training epoch: {100, 300, 500} 1Number of GMM components: {1, 3, 5} 1Number of GMM samples: {64, 128, 256, 512} |