Abstract
Revealing the syntactic structure of sentences in Chinese poses significant challenges for word-level parsers due to the absence of clear word boundaries. To facilitate a transition from word-level to character-level Chinese dependency parsing, this paper proposes modeling latent internal structures within words. In this way, each word-level dependency tree is interpreted as a forest of character-level trees. A constrained Eisner algorithm is implemented to ensure the compatibility of character-level trees, guaranteeing a single root for intra-word structures and establishing inter-word dependencies between these roots. Experiments on Chinese treebanks demonstrate the superiority of our method over both the pipeline framework and previous joint models. A detailed analysis reveals that a coarse-to-fine parsing strategy empowers the model to predict more linguistically plausible intra-word structures.- Anthology ID:
- 2024.findings-acl.173
- Volume:
- Findings of the Association for Computational Linguistics ACL 2024
- Month:
- August
- Year:
- 2024
- Address:
- Bangkok, Thailand and virtual meeting
- Editors:
- Lun-Wei Ku, Andre Martins, Vivek Srikumar
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2943–2956
- Language:
- URL:
- https://aclanthology.org/2024.findings-acl.173
- DOI:
- Cite (ACL):
- Yang Hou and Zhenghua Li. 2024. Character-Level Chinese Dependency Parsing via Modeling Latent Intra-Word Structure. In Findings of the Association for Computational Linguistics ACL 2024, pages 2943–2956, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics.
- Cite (Informal):
- Character-Level Chinese Dependency Parsing via Modeling Latent Intra-Word Structure (Hou & Li, Findings 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.findings-acl.173.pdf