Data-efficient Active Learning for Structured Prediction with Partial Annotation and Self-Training
This work addresses annotation efficiency for structured prediction, which is important for NLP/vision researchers with limited labeling budgets, though it represents an incremental improvement over existing active learning techniques.
The paper tackles the problem of high annotation costs for structured prediction tasks by proposing a method that combines partial annotation with self-training and an adaptive selection ratio. The approach reduces annotation costs by 15-30% compared to full annotation baselines across four structured prediction tasks.
In this work we propose a pragmatic method that reduces the annotation cost for structured label spaces using active learning. Our approach leverages partial annotation, which reduces labeling costs for structured outputs by selecting only the most informative sub-structures for annotation. We also utilize self-training to incorporate the current model's automatic predictions as pseudo-labels for un-annotated sub-structures. A key challenge in effectively combining partial annotation with self-training to reduce annotation cost is determining which sub-structures to select to label. To address this challenge, we adopt an error estimator to adaptively decide the partial selection ratio according to the current model's capability. In evaluations spanning four structured prediction tasks, we show that our combination of partial annotation and self-training using an adaptive selection ratio reduces annotation cost over strong full annotation baselines under a fair comparison scheme that takes reading time into consideration.