Xudong Chen


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A Double-Graph Based Framework for Frame Semantic Parsing
Ce Zheng | Xudong Chen | Runxin Xu | Baobao Chang
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Frame semantic parsing is a fundamental NLP task, which consists of three subtasks: frame identification, argument identification and role classification. Most previous studies tend to neglect relations between different subtasks and arguments and pay little attention to ontological frame knowledge defined in FrameNet. In this paper, we propose a Knowledge-guided Incremental semantic parser with Double-graph (KID). We first introduce Frame Knowledge Graph (FKG), a heterogeneous graph containing both frames and FEs (Frame Elements) built on the frame knowledge so that we can derive knowledge-enhanced representations for frames and FEs. Besides, we propose Frame Semantic Graph (FSG) to represent frame semantic structures extracted from the text with graph structures. In this way, we can transform frame semantic parsing into an incremental graph construction problem to strengthen interactions between subtasks and relations between arguments. Our experiments show that KID outperforms the previous state-of-the-art method by up to 1.7 F1-score on two FrameNet datasets. Our code is availavle at https://github.com/PKUnlp-icler/KID.

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融合知识的多目标词联合框架语义分析模型(Knowledge-integrated Joint Model For Multi-target Frame Semantic Parsing)
Xudong Chen (陈旭东) | Ce Zheng (郑策) | Baobao Chang (常宝宝)
Proceedings of the 21st Chinese National Conference on Computational Linguistics



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Joint Multi-Decoder Framework with Hierarchical Pointer Network for Frame Semantic Parsing
Xudong Chen | Ce Zheng | Baobao Chang
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021