  Human annotation is a time-consuming task that requires a significant amount
of effort. To address this issue, interactive data annotation utilizes an
annotation model to provide suggestions for humans to approve or correct.
However, annotation models trained with limited labeled data are prone to
generating incorrect suggestions, leading to extra human correction effort. To
tackle this challenge, we propose Araida, an analogical reasoning-based
approach that enhances automatic annotation accuracy in the interactive data
annotation setting and reduces the need for human corrections. Araida involves
an error-aware integration strategy that dynamically coordinates an annotation
model and a k-nearest neighbors (KNN) model, giving more importance to KNN's
predictions when predictions from the annotation model are deemed inaccurate.
Empirical studies demonstrate that Araida is adaptable to different annotation
tasks and models. On average, it reduces human correction labor by 11.02%
compared to vanilla interactive data annotation methods.
