Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Document-level Relation Extraction via Subgraph Reasoning
Authors: Xingyu Peng, Chong Zhang, Ke Xu
IJCAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on Doc RED show that SGR outperforms existing models, and further analyses demonstrate that our method is both effective and explainable. Our code is available at https://github.com/Crysta1ovo/SGR. |
| Researcher Affiliation | Academia | Xingyu Peng , Chong Zhang and Ke Xu State Key Lab of Software Development Environment, Beihang University, Beijing, 100191, China EMAIL |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | Yes | Our code is available at https://github.com/Crysta1ovo/SGR. |
| Open Datasets | Yes | We evaluate our model on Doc RED, a large-scale humanannotated dataset for document-level RE constructed from Wikipedia and Wikidata. |
| Dataset Splits | Yes | Doc RED contains 3,053 documents for training, 1,000 for development, and 1,000 for testing, involving 96 relation types, 132,275 entities, and 56,354 relational facts. |
| Hardware Specification | No | No specific hardware details (e.g., GPU models, CPU types, or memory) used for running experiments are provided in the paper. |
| Software Dependencies | No | While the paper mentions using GloVe, Bi LSTM, and AdamW, it does not specify the version numbers for these or any other software libraries, environments, or programming languages used. |
| Experiment Setup | Yes | With setting the batch size to 4, we train our model using Adam W [Loshchilov and Hutter, 2019] optimizer, a linear learning rate scheduler with 6% warmup, and a maximum learning rate of 0.01. All hyperparameters are tuned based on the development set. |