用XML标签联合翻译与标注投影，提升低资源语言的跨语言迁移效果。
Just Use XML: Revisiting Joint Translation and Label Projection
- 通过XML标签实现翻译与标注投影一体化处理
- 在11种语言上提升翻译质量，跨任务最高增益40.2 F1
- 适合需要高效跨语言数据扩展的研究者
标签投影是一种有效的跨语言迁移技术，可将高资源语言的标注样本扩展至低资源语言。现有方法通常将标签投影作为机器翻译后的独立步骤，而此前联合处理的方法被认为会降低翻译质量。我们提出新框架LabelPigeon，通过XML标签联合执行翻译与标签投影。设计直接评估标签投影的方案后发现，LabelPigeon优于基线，并在11种语言中主动提升翻译质量。进一步在203种语言及不同标注复杂度下评估，均显示因额外微调带来的稳定提升。在27种语言和三个下游任务中，相比已有工作实现显著增益，命名实体识别任务最高提升40.2 F1。结果表明，使用XML标记的标签投影能有效且高效地实现标签转移，同时不损害翻译质量。
原文摘要 · Abstract (English)
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.
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