为瓜拉尼语设计以口语为中心的对话智能系统，尊重原住民语言实践。
Designing Explainable Conversational Agentic Systems for Guaraní Speakers
- 构建口语优先的多代理架构，分离语言理解与对话管理
- 支持轮换发言、纠错与共享上下文，契合口头交流习惯
- 适合关注原住民语言主权与文化适配的AI研究者
尽管人工智能与人机交互系统常被宣传为普适解决方案，其设计仍以文本为主，未能充分支持口语语言及原住民社区。本文以巴拉圭官方语言之一的瓜拉尼语为例，论证语言支持若不契合真实的口头实践，便无法真正有效。我们提出替代传统‘文本转语音’流程的口语优先多代理架构，通过将瓜拉尼自然语言理解与对话状态管理、社区主导治理分离开来，构建一个尊重原住民数据主权与双语并存现象的技术框架。研究聚焦于轮换发言、纠错与共享上下文，将其作为交互核心。结论指出，真正的文化扎根需从适应口语语言到文本中心系统，转向将口语对话视为首要设计要求，确保数字生态赋能而非忽视多元语言实践。
原文摘要 · Abstract (English)
Although artificial intelligence (AI) and Human-Computer Interaction (HCI) systems are often presented as universal solutions, their design remains predominantly text-first, underserving primarily oral languages and indigenous communities. This position paper uses Guaraní, an official and widely spoken language of Paraguay, as a case study to argue that language support in AI remains insufficient unless it aligns with lived oral practices. We propose an alternative to the standard "text-to-speech" pipeline, proposing instead an oral-first multi-agent architecture. By decoupling Guaraní natural language understanding from dedicated agents for conversation state and community-led governance, we demonstrate a technical framework that respects indigenous data sovereignty and diglossia. Our work moves beyond mere recognition to focus on turn-taking, repair, and shared context as the primary locus of interaction. We conclude that for AI to be truly culturally grounded, it must shift from adapting oral languages to text-centric systems to treating spoken conversation as a first-class design requirement, ensuring digital ecosystems empower rather than overlook diverse linguistic practices.
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