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..
Knowledge and Cross-Pair Pattern Guided Semantic Matching for Question Answering
Authors: Zihan Xu, Hai-Tao Zheng, Shaopeng Zhai, Dong Wang9370-9377
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments show that KCG is robust against the diversity of Q-A pairs and outperforms the state-of-the-art systems on different answer selection tasks. |
| Researcher Affiliation | Academia | 1Tsinghua Shenzhen International Graduate School, Tsinghua University 2Department of Computer Science and Technology, Tsinghua University, Beijing, China 3School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University |
| Pseudocode | No | The paper describes algorithms but does not provide structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain an explicit statement about the release of source code for the described methodology or a link to a code repository. |
| Open Datasets | Yes | We evaluate our model on two widely adopted QA benchmark datasets: Wiki QA (Yang, Yih, and Meek 2015) and Trec QA (Wang, Smith, and Mitamura 2007). |
| Dataset Splits | Yes | Wiki QA Train 873 8672 12.0 12.2K 21.9M Dev 126 1130 12.4 Test 243 2351 12.5 |
| Hardware Specification | No | The paper does not specify any hardware details such as GPU models, CPU types, or memory used for running the experiments. |
| Software Dependencies | No | The paper mentions tools and models like GloVE, TransE, Freebase, BERT, and Adam, but does not specify any software versions for libraries, frameworks, or operating systems. |
| Experiment Setup | Yes | Table 2: Hyperparameters. Hyperparameter Method Name Definition Intra-Pair Cross-Pair λ Learning rate 0.001 0.01 p Dropout rate 0.2 0.5 L2 L2 normalization 0 0.0005 m Batch size 4 1 w Conv. size [1,2,3,4,5] 1 h Hidden layer size 300 (64) τ Edge threshold 0.95 r Neg. rate 1:1 |