mlsanityData to 13 Oct 2026

Auto311: A Confidence-guided Automated System for Non-emergency Calls

arXiv 2312.14185 ↗First posted 19 Dec 2023cs.CL cs.AI cs.CYcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 13 Oct 2026). Builds on 23 of its 51 references that are in the map. First posted on arXiv 19 Dec 2023.

Builds on

23 of 51 references.

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Cited by year (2026 to 13 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.

  1. 2025LogiDebrief: A Signal-Temporal Logic based Automated Debriefing Approach with Large Language Models Integrationcited by 5
  2. 2026Empowering 9-1-1 Calltaking Training with Generative AI: Experiences and Lessons Learnedcited by 1
  3. 2026PACE: A Personalized Adaptive Curriculum Engine for 9-1-1 Call-taker Trainingcited by 0