  This paper presents a novel and comprehensive solution to enhance both the
robustness and efficiency of question answering (QA) systems through supervised
contrastive learning (SCL). Training a high-performance QA system has become
straightforward with pre-trained language models, requiring only a small amount
of data and simple fine-tuning. However, despite recent advances, existing QA
systems still exhibit significant deficiencies in functionality and training
efficiency. We address the functionality issue by defining four key tasks: user
input intent classification, out-of-domain input detection, new intent
discovery, and continual learning. We then leverage a unified SCL-based
representation learning method to efficiently build an intra-class compact and
inter-class scattered feature space, facilitating both known intent
classification and unknown intent detection and discovery. Consequently, with
minimal additional tuning on downstream tasks, our approach significantly
improves model efficiency and achieves new state-of-the-art performance across
all tasks.
