XNLP: An Interactive Demonstration System for Universal Structured NLP
Hao Fei Meishan Zhang Min Zhang Tat-Seng Chua
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