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Emergency and non-emergency response systems are essential services providedby local governments and critical to protecting lives, the environment, andproperty. The effective handling of (non-)emergency calls is critical forpublic safety and well-being. By reducing the burden through non-emergencycallers, residents in critical need of assistance through 911 will receive afast and effective response. Collaborating with the Department of EmergencyCommunications (DEC) in Nashville, we analyzed 11,796 non-emergency callrecordings and developed Auto311, the first automated system to handle 311non-emergency calls, which (1) effectively and dynamically predicts ongoingnon-emergency incident types to generate tailored case reports during the call;(2) itemizes essential information from dialogue contexts to complete thegenerated reports; and (3) strategically structures system-caller dialogueswith optimized confidence. We used real-world data to evaluate the system'seffectiveness and deployability. The experimental results indicate that thesystem effectively predicts incident type with an average F-1 score of 92.54%.Moreover, the system successfully itemizes critical information from relevantcontexts to complete reports, evincing a 0.93 average consistency scorecompared to the ground truth. Additionally, emulations demonstrate that thesystem effectively decreases conversation turns as the utterance size gets moreextensive and categorizes the ongoing call with 94.49% mean accuracy.

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