Curriculum Learning for Natural Answer Generation
Authors: Cao Liu, Shizhu He, Kang Liu, Jun Zhao
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments demonstrate that CL-NAG outperforms the state-of-the-art, which increases 6.8% and 8.7% in the accuracy for simple and complex questions, respectively. 4 Experiments |
| Researcher Affiliation | Academia | 1 National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China 2 University of Chinese Academy of Sciences, Beijing, 100049, China |
| Pseudocode | No | The paper describes the methodology in text and a diagram (Figure 2) but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper mentions a third-party tool's GitHub link in a footnote ("WBMs are implemented in https://github.com/Maluuba/nlgeval.") but does not provide concrete access or an explicit statement about the availability of its own source code for the described methodology. |
| Open Datasets | Yes | Experimental data is an open real-world CQA dataset, which is from COREQA [He et al., 2017]. |
| Dataset Splits | No | The paper states: "The dataset is divided into simple-QA and complex-QA according to the number of matched knowledge facts, in which simple-QA only matches one grounded fact, and the complex-QA contains multiple grounded facts." It mentions "training data" but does not provide specific percentages or counts for training, validation, and test splits. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions tools and frameworks like "Stanford Parser" and "jieba6 toolkit" but does not provide specific version numbers for any software dependencies needed to replicate the experiment. |
| Experiment Setup | Yes | For purpose of comparison, we design experimental settings as follows. ... FP: Common and target instances are combined by a fixed proportion (we set it to 0.5). ... a minimum threshold for the low term frequency (e.g. 10) could be used to filter such noise. ... a proportion (e.g. 0.5) of short and long answers is set to choose fewer short answers |