World's healthcare systems and mental health agencies face both a growing
demand for youth mental health services, alongside a simultaneous challenge of
limited resources. Given these constraints, this work presents our experience
in the creation and evaluation of the FAIIR (Frontline Assistant: Issue
Identification and Recommendation) tool, an ensemble of domain-adapted and
fine-tuned transformer models, leveraging natural language processing to
identify issues that youth may be experiencing. We explore the technical
development, performance, and validation processes leveraged for the FAIIR tool
in application to situations of frontline crisis response via Kids Help Phone.
Frontline Crisis Responders assign an issue tag from a defined list following
each conversation. Assisting with the identification of issues of relevance
helps reduce the burden on CRs, ensuring that appropriate resources can be
provided and that active rescues and mandatory reporting can take place in
critical situations requiring immediate de-escalation.

本研究介绍了创建和评估 FAIIR（前线助手：问题识别和建议）工具的经验，该工具利用自然语言处理，通过应用到 Kids Help Phone 的前线危机响应情境，利用领域适应和优化的 Transformer 模型识别青少年可能遇到的问题。