Multi-task Active Learning for Pre-trained Transformer-based Models
This work addresses annotation efficiency for NLP researchers and practitioners, but it is incremental as it extends existing active learning methods to a new model type without a major breakthrough.
The paper tackles the problem of high annotation costs in multi-task learning for NLP by applying multi-task active learning (MT-AL) to pre-trained Transformer models, showing that MT-AL effectively reduces annotation efforts compared to single-task selection in various realistic scenarios.
Multi-task learning, in which several tasks are jointly learned by a single model, allows NLP models to share information from multiple annotations and may facilitate better predictions when the tasks are inter-related. This technique, however, requires annotating the same text with multiple annotation schemes which may be costly and laborious. Active learning (AL) has been demonstrated to optimize annotation processes by iteratively selecting unlabeled examples whose annotation is most valuable for the NLP model. Yet, multi-task active learning (MT-AL) has not been applied to state-of-the-art pre-trained Transformer-based NLP models. This paper aims to close this gap. We explore various multi-task selection criteria in three realistic multi-task scenarios, reflecting different relations between the participating tasks, and demonstrate the effectiveness of multi-task compared to single-task selection. Our results suggest that MT-AL can be effectively used in order to minimize annotation efforts for multi-task NLP models.