An Encoder-Decoder Framework Translating Natural Language to Database Queries
Authors: Ruichu Cai, Boyan Xu, Zhenjie Zhang, Xiaoyan Yang, Zijian Li, Zhihao Liang
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The empirical evaluation on real world database and queries show that our approach outperform state-of-the-art solution by a significant margin. |
| Researcher Affiliation | Collaboration | 1 Faculty of Computer, Guangdong University of Technology, China 2 Singapore R&D, Yitu Technology Ltd. |
| Pseudocode | No | The paper describes the techniques in detail but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statement about making the source code available or include a link to a code repository. |
| Open Datasets | Yes | We run our experiments on three databases, namely Geo880, Academic and IMDB. Academic database has 17 tables, collected by Microsoft Academic Search [Roy et al., 2013]. We reuse the queries and natural language descriptions in Geo880 database, and use the standard training/test split as in [Iyer et al., 2017]. |
| Dataset Splits | Yes | All numbers reported in the experiments on MAS and IMDB are average of 5-fold cross validations. We reuse the queries and natural language descriptions in Geo880 database, and use the standard training/test split as in [Iyer et al., 2017]. |
| Hardware Specification | No | The paper mentions training models but does not specify any details about the hardware used (e.g., specific GPU/CPU models, memory amounts). |
| Software Dependencies | Yes | Our model is implemented in TensorFlow 1.2.0. |
| Experiment Setup | Yes | We optimize the hyerparameters in all approaches and use the configuration with best results. The result hyperparameters are listed in Table 3. |