只需使用XML：重新审视联合翻译与标签投影
Just Use XML: Revisiting Joint Translation and Label Projection
AI总结：
本文提出LabelPigeon框架，通过XML标签联合执行翻译与标签投影，实验证明在11种语言中提升翻译质量，跨203种语言和不同标注复杂度下均取得显著改进，NER任务中达到+40.2 F1的提升。
摘要：
标签投影是跨语言迁移的有效技术，通过扩展标注数据集将高资源语言的跨度标注扩展到低资源语言。大多数方法在机器翻译后单独执行标签投影，而先前结合两者的方法报告了翻译质量下降。我们使用LabelPigeon框架重新评估这一结论，该框架通过XML标签联合执行翻译和标签投影。我们设计了直接评估方案，发现LabelPigeon在11种语言中优于基线并主动提升翻译质量。我们进一步评估了203种语言和不同标注复杂度下的翻译质量，发现改进归因于额外的微调。最后，在27种语言和三个下游任务中，我们报告了相对于可比工作的显著跨语言迁移提升，最高达到NER任务的+40.2 F1。总体而言，我们的结果表明，XML标注的标签投影提供了有效且高效的标签转移，而不会损害翻译质量。
英文摘要：
Label projection is an effective technique for cross-lingual transfer, extending span-annotated datasets from a high-resource language to low-resource ones. Most approaches perform label projection as a separate step after machine translation, and prior work that combines the two reports degraded translation quality. We re-evaluate this claim with LabelPigeon, a novel framework that jointly performs translation and label projection via XML tags. We design a direct evaluation scheme for label projection, and find that LabelPigeon outperforms baselines and actively improves translation quality in 11 languages. We further assess translation quality across 203 languages and varying annotation complexity, finding consistent improvement attributed to additional fine-tuning. Finally, across 27 languages and three downstream tasks, we report substantial gains in cross-lingual transfer over comparable work, up to +40.2 F1 on NER. Overall, our results demonstrate that XML-tagged label projection provides effective and efficient label transfer without compromising translation quality.
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。
