Character-Level Chinese Dependency Parsing via Modeling Latent Intra-Word Structure
<|reference_start|>Character-Level Chinese Dependency Parsing via Modeling Latent Intra-Word Structure: 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.<|reference_end|>
@article{hou2024character-level,
    title={Character-Level Chinese Dependency Parsing via Modeling Latent
  Intra-Word Structure},
    author={Yang Hou, Zhenghua Li},
    journal={arXiv preprint arXiv:2406.03772},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.03772},
    primaryClass={cs.CL}
}