INSURE-Dial: 一个面向合规验证和阶段检测的相位感知对话数据集与基准
INSURE-Dial: A Phase-Aware Conversational Dataset & Benchmark for Compliance Verification and Phase Detection
AI总结：
INSURE-Dial是一个面向合规验证和阶段检测的相位感知对话数据集与基准，通过相位结构化标注和两个新评估任务提升语音代理的合规性评估能力。
摘要：
行政电话任务每年从美国医疗保健领域消耗约1万亿美元，2024年超过5亿次保险权益验证电话由人工处理。我们引入INSURE-Dial，据我们所知，这是第一个公开的基准，用于开发和评估具有合规意识的语音代理，用于具有相位感知的电话审计，采用基于跨度的合规验证。数据集包括50个去标识的AI发起的电话，包含实时保险代表（平均每通电话71轮）和1000个合成生成的电话，模拟相同的工作流程。所有电话均用一个相位结构化的JSON模式注释，涵盖IVR导航、患者识别、覆盖状态、药物检查（最多两种药物）和代理识别（CRN），并且每个阶段都根据显式的询问/回答逻辑标记合规性。我们定义了两个新的评估任务：（1）相位边界检测（基于相位特定接受规则的跨度分割）和（2）合规验证（在固定跨度下做出IC/PC决定）。每阶段得分在小规模、低延迟基线中表现良好，但端到端的可靠性受到跨度边界错误的限制。在真实电话中，完整电话的精确分割率较低，显示出对话流畅性与审计级证据之间的差距。
英文摘要：
Administrative phone tasks drain roughly 1 trillion USD annually from U.S. healthcare, with over 500 million insurance-benefit verification calls manually handled in 2024. We introduce INSURE-Dial, to our knowledge the first public benchmark for developing and assessing compliance-aware voice agents for phase-aware call auditing with span-based compliance verification. The corpus includes 50 de-identified, AI-initiated calls with live insurance representatives (mean 71 turns/call) and 1,000 synthetically generated calls that mirror the same workflow. All calls are annotated with a phase-structured JSON schema covering IVR navigation, patient identification, coverage status, medication checks (up to two drugs), and agent identification (CRN), and each phase is labeled for Information and Procedural compliance under explicit ask/answer logic. We define two novel evaluation tasks: (1) Phase Boundary Detection (span segmentation under phase-specific acceptance rules) and (2) Compliance Verification (IC/PC decisions given fixed spans). Per-phase scores are strong across small, low-latency baselines, but end-to-end reliability is constrained by span-boundary errors. On real calls, full-call exact segmentation is low, showing a gap between conversational fluency and audit-grade evidence.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
