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..
End-to-End Entity Linking with Hierarchical Reinforcement Learning
Authors: Lihan Chen, Tinghui Zhu, Jingping Liu, Jiaqing Liang, Yanghua Xiao
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments to show that the proposed method achieves state-of-the-art performance in several EL benchmark datasets. |
| Researcher Affiliation | Collaboration | Lihan Chen1, Tinghui Zhu1, Jingping Liu2, Jiaqing Liang3, Yanghua Xiao1, 4* 1 Shanghai Key Laboratory of Data Science, School of Computer Science, Fudan University 2 East China University of Science and Technology, Shanghai, China 3 School of Data Science, Fudan University, China 4 Fudan-Aishu Cognitive Intelligence Joint Research Center |
| Pseudocode | Yes | Algorithm 1: Hierarchical Policy Optimization for EL |
| Open Source Code | Yes | Our code is publicly available at https://github.com/lhlclhl/he2eel. |
| Open Datasets | Yes | We use the standard English AIDA-Co NLL splits (Hoffart et al. 2011) for training, validation, and in-domain test. |
| Dataset Splits | Yes | We use the standard English AIDA-Co NLL splits (Hoffart et al. 2011) for training, validation, and in-domain test. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for experiments, such as GPU or CPU models. |
| Software Dependencies | No | The paper mentions software components like Longformer, LSTM, and BART, but does not specify their version numbers or other software dependencies with specific versions. |
| Experiment Setup | No | The detailed settings including dataset statistics, training details and hyper-parameters settings are presented in supplementary materials. |