Document-Level Relation Extraction with Adaptive Thresholding and Localized Context Pooling
Authors: Wenxuan Zhou, Kevin Huang, Tengyu Ma, Jing Huang14612-14620
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experiment on three document-level RE benchmark datasets: Doc RED, a recently released large-scale RE dataset, and two datasets CDR and GDA in the biomedical domain. Our ATLOP (Adaptive Thresholding and Localized c Ontext Pooling) model achieves an F1 score of 63.4, and also significantly outperforms existing models on both CDR and GDA. Experiments on three document-level relation extraction datasets, Doc RED (Yao et al. 2019), CDR (Li et al. 2016), and GDA (Wu et al. 2019b), demonstrate that our ATLOP model significantly outperforms the state-of-the-art methods. |
| Researcher Affiliation | Collaboration | Wenxuan Zhou,1* Kevin Huang,2 Tengyu Ma,3 Jing Huang 2 1Department of Computer Science, University of Southern California, Los Angeles, CA 2JD AI Research, Mountain View, CA 3Department of Computer Science, Stanford University, Stanford, CA |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | We have released our code at https://github.com/wzhouad/ATLOP. |
| Open Datasets | Yes | We evaluate our ATLOP model on three public document-level relation extraction datasets. The dataset statistics are shown in Table 1. Doc RED (Yao et al. 2019) is a large-scale crowdsourced dataset for document-level RE. CDR (Li et al. 2016) is a human-annotated dataset in the biomedical domain. GDA (Wu et al. 2019b) is a large-scale dataset in the biomedical domain. |
| Dataset Splits | Yes | Table 1: Statistics of the datasets in experiments. Doc RED # Train 3053 # Dev 1000 # Test 1000. CDR # Train 500 # Dev 500 # Test 500. GDA # Train 23353 # Dev 5839 # Test 1000. We follow Christopoulou, Miwa, and Ananiadou (2019) to split the training set into an 80/20 split as training and development sets. |
| Hardware Specification | Yes | All models are trained with 1 Tesla V100 GPU. |
| Software Dependencies | No | The paper mentions software like Huggingface's Transformers and Apex library, and pre-trained models like BERT-base, RoBERTa-large, and SciBERT, but does not provide specific version numbers for these software dependencies. |
| Experiment Setup | Yes | Table 2: Hyper-parameters in training. Batch size 4, 4, 4, 16. # Epoch 30, 30, 30, 10. lr for encoder 5e-5, 3e-5, 2e-5, 2e-5. lr for classifier 1e-4, 1e-4, 1e-4, 1e-4. Our model is optimized with Adam W (Loshchilov and Hutter 2019) using learning rates {2e 5, 3e 5, 5e 5, 1e 4}, with a linear warmup (Goyal et al. 2017) for the first 6% steps followed by a linear decay to 0. We apply dropout (Srivastava et al. 2014) between layers with rate 0.1, and clip the gradients of model parameters to a max norm of 1.0. |