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.

翻译：当前多语言语义解析（MSP）数据集几乎全部通过将现有资源丰富语言数据集中的话语翻译至目标语言构建而成。然而，人工翻译成本高昂。为降低翻译工作量，本文首次提出面向MSP的主动学习流程（AL-MSP）。AL-MSP仅需翻译现有数据集的子集。我们提出一种创新的选择方法，优先选取能通过更多词汇选择来丰富逻辑形式结构多样性的样本，并设计一种无需额外标注成本的新型超参数调优方法。实验表明，采用理想选择方法时，AL-MSP能够显著降低翻译成本。在两组多语言数据集上，我们采用适当超参数的选择方法在解析性能上优于其他基线方法。