We describe a method for selecting relevant new training data for the
LSTM-based domain selection component of our personal assistant system. Adding
more annotated training data for any ML system typically improves accuracy, but
only if it provides examples not already adequately covered in the existing
data. However, obtaining, selecting, and labeling relevant data is expensive.
This work presents a simple technique that automatically identifies new helpful
examples suitable for human annotation. Our experimental results show that the
proposed method, compared with random-selection and entropy-based methods,
leads to higher accuracy improvements given a fixed annotation budget. Although
developed and tested in the setting of a commercial intelligent assistant, the
technique is of wider applicability.

本研究介绍了一种基于 LSTM 域选择组件的人工智能助理系统中对于选择相关新训练数据的方法，实验结果表明，与随机选择和基于熵的方法相比，在固定标注预算的情况下提供了更高的准确性提升。