GDPNet: Refining Latent Multi-View Graph for Relation Extraction
Authors: Fuzhao Xue, Aixin Sun, Hao Zhang, Eng Siong Chng14194-14202
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | On Dialog RE and TACRED, we show that GDPNet achieves the best performance on dialogue-level RE, and comparable performance with the state-of-the-arts on sentence-level RE. |
| Researcher Affiliation | Academia | 1 School of Computer Science and Engineering, Nanyang Technological University, Singapore 2 Institute of High Performance Computing, Agency for Science, Technology and Research, Singapore |
| Pseudocode | No | No pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | Yes | Our code is available at https://github.com/Xue Fuzhao/GDPNet. |
| Open Datasets | Yes | On Dialog RE and TACRED, we show that GDPNet achieves the best performance on dialogue-level RE, and comparable performance with the state-of-the-arts on sentence-level RE. |
| Dataset Splits | Yes | Model Dev set Test set F1 (σ) F1c (σ) F1 (σ) F1c (σ) |
| Hardware Specification | Yes | All experiments are conducted on a desktop with Intel i7-8750H CPU, DDR4 16GB memory, and a single NVIDIA Ge Force RTX 1070 GPU. We also reproduced our results on Quadro RTX 8000 GPU. |
| Software Dependencies | Yes | The GDPNet is implemented by using Py Torch 1.4 with CUDA 10.1. Our implementation also uses the Soft DTW2 toolkit. |
| Experiment Setup | Yes | The hyper-parameter settings on the two datasets, Dialog RE and TACRED, are listed as follows. Parameter Dialog RE TACRED Epoch 20 10 Batch Size 24 32 Learning rate 3e-5 2e-5 Dropout 0.5 0.5 Hidden units of graph 300 300 Number of views 3 3 Number of DTWPool layers 3 3 Pooling ratio lower bound 0.7 0.8 Weight of Soft DTW loss 1e-6 2e-4 |