We introduce APEL, a new framework that enables non-programmers to indirectly annotate natural language utterances with executable meaning representations, such as SQL programs. Based on a natural language utterance, we first run a seed semantic parser to generate a prior over a list of candidate programs. To obtain information about which candidate is correct, we synthesize an input on which the more likely programs tend to produce different outputs, and ask an annotator which output is appropriate for the utterance. Hence, the annotator does not have to directly inspect the programs. To further reduce effort required from annotators, we aim to synthesize simple input databases that nonetheless have high information gain. With human annotators and Bayesian inference to handle annotation errors, we outperform Codex’s top-1 performance (59%) and achieve the same accuracy as the original expert annotators (75%), by soliciting answers for each utterance on only 2 databases with an average of 9 records each. In contrast, it would be impractical to solicit outputs on the original 30K-record databases provided by SPIDER.<\/p>\n","protected":false},"excerpt":{"rendered":"
We introduce APEL, a new framework that enables non-programmers to indirectly annotate natural language utterances with executable meaning representations, such as SQL programs. Based on a natural language utterance, we first run a seed semantic parser to generate a prior over a list of candidate programs. To obtain information about which candidate is correct, we 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