Data augmentation is an effective solution to data scarcity in low-resource
scenarios. However, when applied to token-level tasks such as NER, data
augmentation methods often suffer from token-label misalignment, which leads to
unsatsifactory performance. In this work, we propose Masked Entity Language
Modeling (MELM) as a novel data augmentation framework for low-resource NER. To
alleviate the token-label misalignment issue, we explicitly inject NER labels
into sentence context, and thus the fine-tuned MELM is able to predict masked
entity tokens by explicitly conditioning on their labels. Thereby, MELM
generates high-quality augmented data with novel entities, which provides rich
entity regularity knowledge and boosts NER performance. When training data from
multiple languages are available, we also integrate MELM with code-mixing for
further improvement. We demonstrate the effectiveness of MELM on monolingual,
cross-lingual and multilingual NER across various low-resource levels.
Experimental results show that our MELM presents substantial improvement over
the baseline methods.

使用 MELM 作为一种新的数据增强框架，通过显式条件化于标签来预测掩码实体标记，从而为低资源 NER 生成高质量的增强数据，并通过与混合代码结合进一步提高了 MELM 的有效性，在单语，跨语言和多语言 NER 方面均取得了实验结果的显著改进。