Holistic Exploration on Universal Decompositional Semantic Parsing:
  Architecture, Data Augmentation, and LLM Paradigm
Hexuan Deng Xin Zhang Meishan Zhang Xuebo Liu Min Zhang
In this paper, we conduct a holistic exploration of the Universal
Decompositional Semantic (UDS) Parsing. We first introduce a cascade model for
UDS parsing that decomposes the complex parsing task into semantically
appropriate subtasks. Our approach outperforms the prior models, while
significantly reducing inference time. We also incorporate syntactic
information and further optimized the architecture. Besides, different ways for
data augmentation are explored, which further improve the UDS Parsing. Lastly,
we conduct experiments to investigate the efficacy of ChatGPT in handling the
UDS task, revealing that it excels in attribute parsing but struggles in
relation parsing, and using ChatGPT for data augmentation yields suboptimal
results. Our code is available at https://github.com/hexuandeng/HExp4UDS.