Voice-controlled dialog systems have become immensely popular due to their ability to perform a wide range of actions in response to diverse user queries. These agents possess a predefined set of skills or intents to fulfill specific user tasks. But every system has its own limitations. There are instances where, even for known intents, if any model exhibits low confidence, it results in rejection of utterances that necessitate manual annotation. Additionally, as time progresses, there may be a need to retrain these agents with new intents from the system-rejected queries to carry out additional tasks. Labeling all these emerging intents and rejected utterances over time is impractical, thus calling for an efficient mechanism to reduce annotation costs. In this paper, we introduce IDALC (Intent Detection and Active Learning based Correction), a semi-supervised framework designed to detect user intents and rectify system-rejected utterances while minimizing the need for human annotation. Empirical findings on various benchmark datasets demonstrate that our system surpasses baseline methods, achieving a 5-10% higher accuracy and a 4-8% improvement in macro-F1. Remarkably, we maintain the overall annotation cost at just 6-10% of the unlabelled data available to the system. The overall framework of IDALC is shown in Fig. 1

翻译：语音控制对话系统因其能够响应多样化用户查询而执行广泛操作，已变得极为普及。这些智能体具备预定义的技能集或意图集以完成特定用户任务。然而，每个系统都存在固有局限性。在某些情况下，即使对于已知意图，若模型表现出较低置信度，仍会导致需要人工标注的话语被系统拒绝。此外，随着时间的推移，可能需要利用系统拒绝查询中的新意图重新训练这些智能体，以执行额外任务。持续标注所有新出现的意图和拒绝话语并不现实，因此需要一种高效机制来降低标注成本。本文提出IDALC（基于意图检测与主动学习校正的半监督框架），该框架旨在检测用户意图并校正系统拒绝的话语，同时最大限度减少人工标注需求。在多个基准数据集上的实验结果表明，我们的系统优于基线方法，准确率提升5-10%，宏观F1分数提高4-8%。值得注意的是，我们将整体标注成本控制在系统可用未标注数据量的6-10%。IDALC的整体框架如图1所示。