XNLP: An Interactive Demonstration System for Universal Structured NLP

Hao Fei, Meishan Zhang, Min Zhang, Tat-Seng Chua

Abstract
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. 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 system, where we leverage 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, offering a unified platform for exploring diverse XNLP tasks in the community.
Anthology ID:
2024.acl-demos.3
Volume:
Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)
Month:
August
Year:
2024
Address:
Bangkok, Thailand
Editors:
Yixin Cao, Yang Feng, Deyi Xiong
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
19–30
Language:
URL:
https://aclanthology.org/2024.acl-demos.3
DOI:
10.18653/v1/2024.acl-demos.3
Bibkey:
Cite (ACL):
Hao Fei, Meishan Zhang, Min Zhang, and Tat-Seng Chua. 2024. XNLP: An Interactive Demonstration System for Universal Structured NLP. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations), pages 19–30, Bangkok, Thailand. Association for Computational Linguistics.
Cite (Informal):
XNLP: An Interactive Demonstration System for Universal Structured NLP (Fei et al., ACL 2024)
Copy Citation:
PDF:
https://preview.aclanthology.org/autopr/2024.acl-demos.3.pdf
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