Active Learning++: Incorporating Annotator's Rationale using Local Model Explanation

TL;DR

Active Learning++ enhances active learning by integrating annotator rationales through feature importance rankings, improving query selection accuracy and outperforming traditional methods.

Contribution

It introduces a novel method to incorporate annotator rationales into active learning using local model explanations and a weighted disagreement measure.

Findings

Significantly outperforms vanilla active learning frameworks.

Effectively incorporates annotator rationales into query selection.

Applicable to various ML models using model-agnostic explanations.

Abstract

We propose a new active learning (AL) framework, Active Learning++, which can utilize an annotator's labels as well as its rationale. Annotators can provide their rationale for choosing a label by ranking input features based on their importance for a given query. To incorporate this additional input, we modified the disagreement measure for a bagging-based Query by Committee (QBC) sampling strategy. Instead of weighing all committee models equally to select the next instance, we assign higher weight to the committee model with higher agreement with the annotator's ranking. Specifically, we generated a feature importance-based local explanation for each committee model. The similarity score between feature rankings provided by the annotator and the local model explanation is used to assign a weight to each corresponding committee model. This approach is applicable to any kind of ML model using model-agnostic techniques to generate local explanation such as LIME. With a simulation study, we show that our framework significantly outperforms a QBC based vanilla AL framework.