The Best of Both Worlds: Combining Human and Machine Translations for
  Multilingual Semantic Parsing with Active Learning
Zhuang Li Lizhen Qu Philip R. Cohen Raj V. Tumuluri Gholamreza Haffari
Multilingual semantic parsing aims to leverage the knowledge from the
high-resource languages to improve low-resource semantic parsing, yet commonly
suffers from the data imbalance problem. Prior works propose to utilize the
translations by either humans or machines to alleviate such issues. However,
human translations are expensive, while machine translations are cheap but
prone to error and bias. In this work, we propose an active learning approach
that exploits the strengths of both human and machine translations by
iteratively adding small batches of human translations into the
machine-translated training set. Besides, we propose novel aggregated
acquisition criteria that help our active learning method select utterances to
be manually translated. Our experiments demonstrate that an ideal utterance
selection can significantly reduce the error and bias in the translated data,
resulting in higher parser accuracies than the parsers merely trained on the
machine-translated data.