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 [1].
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. |