Active Learning for Domain Classification in a Commercial Spoken Personal Assistant

TL;DR

This paper introduces an active learning method to efficiently select new training data for improving domain classification in a commercial personal assistant, reducing annotation costs while increasing accuracy.

Contribution

The work presents a simple, automatic technique for identifying valuable new training examples, outperforming random and entropy-based selection methods.

Findings

The proposed method yields higher accuracy improvements with the same annotation budget.

It outperforms random and entropy-based data selection methods.

The technique is applicable beyond the specific assistant system studied.

Abstract

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.