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..
Community-Based Question Answering via Asymmetric Multi-Faceted Ranking Network Learning
Authors: Zhou Zhao, Hanqing Lu, Vincent Zheng, Deng Cai, Xiaofei He, Yueting Zhuang
AAAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The extensive experiments on a large-scale dataset from a real world CQA site show that our method achieves better performance than other state-of-the-art solutions to the problem. We evaluate the performance of our method using the Quora dataset in (Zhao et al. 2015), which is obtained from a popular question answering site, Quora. |
| Researcher Affiliation | Collaboration | Zhou Zhao,1 Hanqing Lu,1 Vincent W. Zheng,2 Deng Cai,3 Xiaofei He,3 Yueting Zhuang1 1College of Computer Science, Zhejiang University 2Advanced Digital Sciences Center, Singapore 3State Key Lab of CAD&CG, Zhejiang University EMAIL, EMAIL, EMAIL |
| Pseudocode | No | No explicit pseudocode or algorithm blocks were found. |
| Open Source Code | No | The paper mentions using code for other methods but does not provide concrete access to the source code for their own proposed method (AMRNL). |
| Open Datasets | Yes | We evaluate the performance of our method using the Quora dataset in (Zhao et al. 2015), which is obtained from a popular question answering site, Quora. |
| Dataset Splits | Yes | We use the ο¬rst 60%, 70% and 80% posted questions as training set, other 10% for validation and the remaining 10% for testing. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory specifications) were mentioned for running experiments. |
| Software Dependencies | No | No specific software dependencies with version numbers (e.g., library names with versions) were mentioned. |
| Experiment Setup | No | While parameters like embedding dimension and Ξ» are varied, specific training hyperparameters such as learning rate, batch size, or number of epochs are not explicitly stated in the main text. |