Non-Programmers Can Label Programs Indirectly via Active Examples: A Case Study with Text-to-SQL

Can non-programmers annotate natural language utterances with complex programs that represent their meaning? We introduce APEL, a framework in which non-programmers select among candidate programs generated by a seed semantic parser (e.g., Codex). Since they cannot understand the candidate programs,...

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Bibliographic Details
Published inarXiv.org
Main Authors Zhong, Ruiqi, Snell, Charlie, Klein, Dan, Eisner, Jason
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 23.10.2023
Subjects
Annotations
Bayesian analysis
Natural language
Natural language (computers)
Statistical inference
Synthesis
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Summary:Can non-programmers annotate natural language utterances with complex programs that represent their meaning? We introduce APEL, a framework in which non-programmers select among candidate programs generated by a seed semantic parser (e.g., Codex). Since they cannot understand the candidate programs, we ask them to select indirectly by examining the programs' input-ouput examples. For each utterance, APEL actively searches for a simple input on which the candidate programs tend to produce different outputs. It then asks the non-programmers only to choose the appropriate output, thus allowing us to infer which program is correct and could be used to fine-tune the parser. As a first case study, we recruited human non-programmers to use APEL to re-annotate SPIDER, a text-to-SQL dataset. Our approach achieved the same annotation accuracy as the original expert annotators (75%) and exposed many subtle errors in the original annotations.
ISSN:2331-8422