AutoNLU：检测、找出根源并修复NLU模型错误
AutoNLU: Detecting, root-causing, and fixing NLU model errors
摘要：
在生产环境中提高自然语言理解（NLU）模型的质量，更具体地说，提高面向任务的语义解析模型的质量是一项繁琐的任务。在这项工作中，我们提出了一个名为AutoNLU的系统，我们设计它来扩展NLU质量改进过程。它将自动化添加到三个关键步骤：检测，归因和纠正模型错误，即，虫子我们检测到的失败任务是随机抽样的四倍，发现即使是一个简单的主动学习抽样方法在未校准的模型上也是非常有效的。AutoNLU工具使语言学家能够修复比以前的手动过程多十倍的语义解析错误，自动纠正65%的已识别错误。
英文摘要：
Improving the quality of Natural Language Understanding (NLU) models, and more specifically, task-oriented semantic parsing models, in production is a cumbersome task. In this work, we present a system called AutoNLU, which we designed to scale the NLU quality improvement process. It adds automation to three key steps: detection, attribution, and correction of model errors, i.e., bugs. We detected four times more failed tasks than with random sampling, finding that even a simple active learning sampling method on an uncalibrated model is surprisingly effective for this purpose. The AutoNLU tool empowered linguists to fix ten times more semantic parsing bugs than with prior manual processes, auto-correcting 65% of all identified bugs.
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。
