Model-based Interactive Semantic Parsing:A Unified Framework and A Text-to-SQL Case Study
Table 10: Comparison of error detectors for SQLNet with a target average number of questions on WikiSQL Test set.
['[BOLD] Avg. #q', '[BOLD] Probability-based  [BOLD] Accqm', '[BOLD] Probability-based  [BOLD] Accex', '[BOLD] Dropout-based  [BOLD] Accqm', '[BOLD] Dropout-based  [BOLD] Accex']
Specifically, for each base semantic parser and each kind of error detector, we tune its decision threshold (i.e., p∗ and s∗) such that the resulting average number of questions (“actual budget”) is as close to the target as possible. In practice, we relax the actual budget to be within ±0.015 of the target budget, which empirically leads to merely negligible variance.