@article{zhu-xu-2026-encoding,
    title = "Encoding Logical Relations of {C}hinese Complex Sentences within the {U}niversal {D}ependencies Framework",
    author = "Zhu, Hongpu  and
      Xu, Hongzhi",
    editor = "Piperidis, Stelios  and
      Bel, N{\'u}ria  and
      van den Heuvel, Henk  and
      Ide, Nancy  and
      Krek, Simon  and
      Toral, Antonio",
    journal = "International Conference on Language Resources and Evaluation",
    volume = "main",
    month = may,
    year = "2026",
    address = "Palma de Mallorca, Spain",
    publisher = "ELRA Language Resource Association",
    url = "https://preview.aclanthology.org/ingest-lrec/2026.lrec-main.910/",
    pages = "11620--11630",
    abstract = "Clauses in complex sentences always entail certain logical relations such as conjunctive, causative, and concessive. Such logical relations, however, are not properly represented in the universal dependencies (UD) framework, being collapsed into a adverbial clause (advcl) or clausal complement (ccomp) relation between clausal heads. This study extends the UD framework by encoding 13 logical relations. With the new framework, which is structurally identical to UD, we construct a training corpus containing about 1,769 sentences extracted from Chinese newswire and annotated an existing Chinese corpus (GSD-simp test) in UD as a test set. We trained a BERT-based biaffine parser and fine-tuned the Qwen-3 model with the training corpus and evaluated the models on the UD test data. They are compared against four general purpose LLMs including GPT-4o, GPT-5, Claude 4 and DeepSeek V3.2. We find that the fine-tuned Qwen-3-8B model achieves a UAS/LAS of 0.840/0.757, higher than the BERT-based parser and the general purpose LLMs. The results confirm the feasibility of our framework and highlight the inherent challenges of parsing hierarchical and implicit inter-clause relations."
}