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 | Conference PDF | Archive PDF | Plain Text | 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 {zhaozhou, lhq110, yzhuang}@zju.edu.cn, vincent.zheng@adsc.com.sg, {dengcai, xiaofeihe}@gmail.com |
| 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 first 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 λ are varied, specific training hyperparameters such as learning rate, batch size, or number of epochs are not explicitly stated in the main text. |