ClinicalAligner26AM: 用于数据集翻译的跨语言对齐器；来自MultiClinCorpus共享任务的证据
ClinicalAligner26AM: A Cross-Lingual Aligner for Dataset Translation; Evidences from the MultiClinCorpus Shared Task
AI总结：
提出ClinicalAligner26AM，一种基于ClinicalEncoder26AM初始化的生物医学临床文本多语言对齐模型，通过Sinkhorn-Knop最优传输融合多级信号构建软对齐目标，在MultiClinCorpus任务中跨语言投影实体标注，字符加权F1超0.95。
摘要：
词级跨语言对齐对于标注投影、翻译审计和跨语言忠实度估计至关重要，然而现有的神经对齐器很少适应专业领域。在本文中，我们介绍了ClinicalAligner26AM，这是一个从ClinicalEncoder26AM初始化的大上下文多语言对齐模型，用于生物医学和临床文本。我们的训练方法受AWESoME Align启发。我们通过使用Sinkhorn-Knop最优传输对为平行临床文本和对话建立的成本矩阵进行锐化，该矩阵融合了句子级、短语级和词元级信号，从而构建软对齐目标。我们通过鼓励学生对齐器的朴素余弦词元相似度分数匹配该目标，直接将锐化后的对齐矩阵蒸馏到学生对齐器中。在推理时，我们通过学习的词元对齐矩阵投影源跨度分数，并解码目标文本中最长有效的高分跨度，可选地由附录B中总结的MultiClinNER预测支持。我们在MultiClinCorpus共享任务上评估CA26AM，该任务将西班牙语临床实体标注投影到六种目标语言中。我们提交的两个系统在所有语言和实体类型中分别排名第一和第二，几乎所有设置下的字符加权F1分数均高于0.95。
英文摘要：
Word-level cross-lingual alignment is central to annotation projection, translation auditing, and cross-lingual faithfulness estimation, yet existing neural aligners are rarely adapted to specialized domains. In this paper, we introduce ClinicalAligner26AM, a large-context multilingual aligner model for biomedical and clinical text initialized from ClinicalEncoder26AM. Our training recipe is inspired by AWESoME Align. We build our soft alignment target by sharpening with Sinkhorn-Knop optimal transport a cost matrix established for parallel clinical texts and conversations through the fusion of sentence-level, phrase-level, and token-level signals. We distill this sharpened alignment matrix directly into our student aligner, by encouraging its naive cosine-based token similarity scores to match this target. At inference time, we project source-span scores through the learned token alignment matrix and decode the longest valid high-scoring span in the target text, optionally supported by MultiClinNER predictions summarized in Appendix B. We evaluate CA26AM on the MultiClinCorpus shared task, which projects Spanish clinical entity annotations into six target languages. Our two submitted systems ranked respectively first and second across all languages and entity types, with character-weighted F1 scores above 0.95 in nearly all settings.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
