Active Learning for Multilingual Semantic Parser
Zhuang Li, Gholamreza Haffari · 2023
Current multilingual semantic parsing (MSP) datasets are almost all collected by translating the utterances in the existing datasets from the resource-rich language to the target language.However, manual translation is costly.To reduce the translation effort, this paper proposes the first active learning procedure for MSP (AL-MSP).AL-MSP selects only a subset from the existing datasets to be translated.We also propose a novel selection method that prioritizes the examples diversifying the logical form structures with more lexical choices, and a novel hyperparameter tuning method that needs no extra annotation cost.Our experiments show that AL-MSP significantly reduces translation costs with ideal selection methods.Our selection method with proper hyperparameters yields better parsing performance than the other baselines on two multilingual datasets.