Knowledge and Cross-Pair Pattern Guided Semantic Matching for Question Answering
Authors: Zihan Xu, Hai-Tao Zheng, Shaopeng Zhai, Dong Wang9370-9377
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | 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 |