Papers › Natural Language to Structured Query Generation via Meta-Learning
Natural Language to Structured Query Generation via Meta-Learning
Po-Sen Huang, Chenglong Wang, Rishabh Singh, Wen-tau Yih, Xiaodong He
In conventional supervised training, a model is trained to fit all the training examples. However, having a monolithic model may not always be the best strategy, as examples could vary widely. In this work, we explore a different learning protocol that treats each example as a unique pseudo-task, by reducing the original learning problem to a few-shot meta-learning scenario with the help of a domain-dependent relevance function. When evaluated on the WikiSQL dataset, our approach leads to faster convergence and achieves 1.1%-5.4% absolute accuracy gains over the non-meta-learning counterparts.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Code Generation | WikiSQL | PT-MAML (Huang et al., 2018) | Exact Match Accuracy | 62.8 | #7 of 10 | Archive leaderboard | report |
| Code Generation | WikiSQL | PT-MAML (Huang et al., 2018) | Execution Accuracy | 68.0 | #7 of 10 | Archive leaderboard | report |
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