Emergency and non-emergency response systems are essential services provided by local governments and critical to protecting lives, the environment, and property. The effective handling of (non-)emergency calls is critical for public safety and well-being. By reducing the burden through non-emergency callers, residents in critical need of assistance through 911 will receive a fast and effective response. Collaborating with the Department of Emergency Communications (DEC) in Nashville, we analyzed 11,796 non-emergency call recordings and developed Auto311, the first automated system to handle 311 non-emergency calls, which (1) effectively and dynamically predicts ongoing non-emergency incident types to generate tailored case reports during the call; (2) itemizes essential information from dialogue contexts to complete the generated reports; and (3) strategically structures system-caller dialogues with optimized confidence. We used real-world data to evaluate the system's effectiveness and deployability. The experimental results indicate that the system effectively predicts incident type with an average F-1 score of 92.54%. Moreover, the system successfully itemizes critical information from relevant contexts to complete reports, evincing a 0.93 average consistency score compared to the ground truth. Additionally, emulations demonstrate that the system effectively decreases conversation turns as the utterance size gets more extensive and categorizes the ongoing call with 94.49% mean accuracy.

翻译：紧急与非紧急响应系统是地方政府提供的基础服务，对于保护生命、环境和财产至关重要。有效处理（非）紧急电话直接关系到公共安全与福祉。通过减少非紧急电话对应急资源的占用，真正需要通过911求助的危重居民将获得快速有效的响应。我们与纳什维尔市紧急通信部（DEC）合作，分析了11,796条非紧急电话录音，开发了Auto311——首个用于处理311非紧急电话的自动化系统。该系统能够：（1）在通话过程中动态且高效地预测正在发生的非紧急事件类型，从而生成定制化案件报告；（2）从对话语境中提取关键信息以完善生成的报告；（3）通过优化置信度策略结构化地构建系统与呼叫者之间的对话。我们采用真实世界数据评估了系统的有效性及可部署性。实验结果表明，该系统在事件类型预测任务上平均F1分数达92.54%。此外，系统能从相关语境中成功提取关键信息以补全报告，与真实标注数据相比平均一致性得分为0.93。仿真结果进一步显示，随着话语规模增加，系统能有效减少对话轮次，并以94.49%的平均准确率对当前通话进行分类。