@inproceedings{liang-chen-2026-escaping,
    title = "Escaping the Probability Trap: Mitigating Semantic Drift in {C}antonese-{M}andarin Translation",
    author = "Liang, Yuzhi  and
      Chen, Fangqi",
    editor = "Hettiarachchi, Hansi  and
      Ranasinghe, Tharindu  and
      Plum, Alistair  and
      Rayson, Paul  and
      Mitkov, Ruslan  and
      Gaber, Mohamed  and
      Premasiri, Damith  and
      Tan, Fiona Anting  and
      Uyangodage, Lasitha",
    booktitle = "Proceedings of the Second Workshop on Language Models for Low-Resource Languages ({L}o{R}es{LM} 2026)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/manual-author-scripts/2026.loreslm-1.41/",
    pages = "471--483",
    ISBN = "979-8-89176-377-7",
    abstract = "Fine-tuning multilingual models for low-resource dialect translation frequently encounters a ``plausibility over faithfulness'' dilemma, resulting in severe semantic drift on dialect-specific tokens. We term this phenomenon the ``Probability Trap,'' where models prioritize statistical fluency over semantic fidelity. To address this, we propose MVS-Rank (Multi-View Scoring Reranking), a generate-then-rerank framework that decouples evaluation from generation. Our method assesses translation candidates through three complementary perspectives: (1) Source-Side Faithfulness via a Reverse Translation Model to anchor semantic fidelity; (2) Local Fluency using Masked Language Models to ensure syntactic precision; and (3) Global Fluency leveraging Large Language Models to capture discourse coherence. Extensive experiments on Cantonese-Mandarin benchmarks demonstrate that MVS-Rank achieves state-of-the-art performance, significantly outperforming strong fine-tuning baselines by effectively rectifying hallucinations while maintaining high fluency."
}