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.

翻译：本文提出了一种新颖且全面的解决方案，通过监督对比学习（SCL）同时提升问答系统的鲁棒性与效率。借助预训练语言模型，训练高性能问答系统已变得简单直接，仅需少量数据和简单微调即可实现。然而，尽管近期研究取得了进展，现有问答系统在功能性与训练效率方面仍存在显著不足。我们通过定义四个关键任务来解决功能性问题：用户输入意图分类、域外输入检测、新意图发现以及持续学习。随后，我们采用基于SCL的统一表示学习方法，高效构建类内紧凑、类间分散的特征空间，从而促进已知意图分类与未知意图的检测及发现。因此，仅需对下游任务进行极少量额外调优，我们的方法即可显著提升模型效率，并在所有任务中取得新的最优性能。