用强化学习选关键文本，减少人工标注错误，提升流式分析效果
ORIS: Online Active Learning Using Reinforcement Learning-based Inclusive Sampling for Robust Streaming Analytics System
- 基于深度Q网络设计动态采样策略，主动筛选高价值文档
- 在情感识别任务中，显著降低人工标注误差并提升模型性能
- 适合标注资源有限的实时数据系统，尤其需人机协同的场景
有效的标注数据收集在构建和优化鲁棒的流式分析系统中至关重要。然而，持续标注文档以筛选相关信息面临标注预算有限或高质量标签不足等挑战。亟需高效的人机协同机器学习（HITL-ML）设计来提升流式分析系统性能。一种典型的HITL-ML方法是在线主动学习，通过迭代选择少量最具信息量的文档进行标注，以增强机器学习模型表现。但此类算法的性能可能因人工标注错误而受损。为此，我们提出ORIS：一种基于强化学习的包容性采样在线主动学习方法，用于提升流式分析系统的鲁棒性。ORIS采用深度Q网络（DQN）构建新型采样策略，旨在最小化标注过程中的误判，并提升模型性能。我们在情感识别任务上评估了该方法，结果表明其在人工标注准确率与模型性能方面均优于传统基线方法。
原文摘要 · Abstract (English)
Effective labeled data collection plays a critical role in developing and fine-tuning robust streaming analytics systems. However, continuously labeling documents to filter relevant information poses significant challenges like limited labeling budget or lack of high-quality labels. There is a need for efficient human-in-the-loop machine learning (HITL-ML) design to improve streaming analytics systems. One particular HITL- ML approach is online active learning, which involves iteratively selecting a small set of the most informative documents for labeling to enhance the ML model performance. The performance of such algorithms can get affected due to human errors in labeling. To address these challenges, we propose ORIS, a method to perform Online active learning using Reinforcement learning-based Inclusive Sampling of documents for labeling. ORIS aims to create a novel Deep Q-Network-based strategy to sample incoming documents that minimize human errors in labeling and enhance the ML model performance. We evaluate the ORIS method on emotion recognition tasks, and it outperforms traditional baselines in terms of both human labeling performance and the ML model performance.
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