两全其美：结合人工与机器翻译的主动学习多语言语义解析
The Best of Both Worlds: Combining Human and Machine Translations for Multilingual Semantic Parsing with Active Learning
AI总结：
提出一种结合人工与机器翻译的主动学习方法，通过聚合获取标准迭代选择话语进行人工翻译，显著降低翻译误差与偏差，提升多语言语义解析器准确率。
摘要：
多语言语义解析旨在利用高资源语言的知识来改善低资源语义解析，但通常面临数据不平衡问题。先前的研究提出利用人工或机器翻译来缓解这些问题。然而，人工翻译昂贵，而机器翻译虽廉价却易产生误差和偏差。在本研究中，我们提出一种主动学习方法，通过迭代地将小批量人工翻译加入机器翻译训练集，来发挥两者的优势。此外，我们提出新颖的聚合获取标准，帮助主动学习方法选择需人工翻译的话语。实验表明，理想的话语选择能显著减少翻译数据中的误差和偏差，使解析器的准确率高于仅在机器翻译数据上训练的解析器。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
