Structured Natural Language Processing (XNLP) is an important subset of NLP that entails understanding the underlying semantic or syntactic structure of texts, which serves as a foundational component for many downstream applications. Despite certain recent efforts to explore universal solutions for specific categories of XNLP tasks, a comprehensive and effective approach for unifying all XNLP tasks long remains underdeveloped. In the meanwhile, while XNLP demonstration systems are vital for researchers exploring various XNLP tasks, existing platforms can be limited to, e.g., supporting few XNLP tasks, lacking interactivity and universalness. To this end, we propose an advanced XNLP demonstration platform, where we propose leveraging LLM to achieve universal XNLP, with one model for all with high generalizability. Overall, our system advances in multiple aspects, including universal XNLP modeling, high performance, interpretability, scalability, and interactivity, providing a unified platform for exploring diverse XNLP tasks in the community. XNLP is online: https://xnlp.haofei.vip

翻译：结构化自然语言处理（XNLP）是自然语言处理的重要子领域，其核心在于理解文本底层的语义或句法结构，为众多下游应用提供基础性支撑。尽管近期已有部分工作尝试探索针对特定XNLP任务类别的通用解决方案，但如何全面且有效地统一所有XNLP任务始终缺乏深入研究。与此同时，尽管XNLP演示系统对研究者探索各类XNLP任务至关重要，现有平台仍存在局限——例如仅支持少量XNLP任务、缺乏交互性与通用性。为此，我们提出一个先进的XNLP演示平台，通过引入大语言模型（LLM）实现通用化XNLP，即采用单一模型完成所有任务并具备高泛化能力。整体而言，我们的系统在通用XNLP建模、高性能、可解释性、可扩展性及交互性等多个方面取得进展，为社区探索多样化XNLP任务提供了统一平台。XNLP在线访问：https://xnlp.haofei.vip