MIGA: A Unified Multi-Task Generation Framework for Conversational Text-to-SQL
Authors: Yingwen Fu, Wenjie Ou, Zhou Yu, Yue Lin
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate MIGA on two conversational text-to-SQL benchmarks Spar C and Co SQL. Comparisons against several advanced approaches show that MIGA tends to achieve state-of-the-art (SOTA) in two datasets based on Interaction Match (IM) accuracy. Extensive analyses provide some new insights into the proposed framework and some takeaways for future conversational text-to-SQL studies. |
| Researcher Affiliation | Collaboration | 1 Guangdong University of Foreign Studies, Guangzhou, China 2 Net Ease Games AI Lab, Guangzhou, China 3 Columbia University |
| Pseudocode | Yes | Algorithm 1: Training Process of MIGA |
| Open Source Code | No | The paper mentions using Hugging Face's Transformers and PyTorch, with links to their respective repositories and the T5 model, but does not provide a link or explicit statement for the open-sourcing of MIGA's specific implementation code. |
| Open Datasets | Yes | We use context-independent dataset Spider (Yu et al. 2018), conversational datasets Spar C (Yu et al. 2019b), Co SQL (Yu et al. 2019a) for the pre-training stage. |
| Dataset Splits | Yes | Detailed data statistics on training and evaluation. Since Spider is a single-turn dataset, experimental data involving interactions are treated as ignored for this dataset (marked with / ). |
| Hardware Specification | Yes | We implement MIGA based on Hugging Face s Transformers1 with Py Torch2 in 2 NVIDIA A100 GPUs. |
| Software Dependencies | No | The paper mentions using 'Hugging Face s Transformers' and 'Py Torch' and initializing with 'T5 model of 3B size', but does not specify their version numbers (e.g., PyTorch 1.9 or Transformers 4.x). |
| Experiment Setup | Yes | Epochs and learning rates for 2 training steps are set as (15, 50) and (1e-4, 5e-5). Batch size and optimizer are set as 64, and Ada Factor (Shazeer and Stern 2018) respectively. Following BERT (Kenton and Toutanova 2019), β in SQL perturbation is 15%. As for the other probability α, we set it as 0.15 in this paper after searching. |