Recent advances in named entity recognition (NER) have pushed the boundary of
the task to incorporate visual signals, leading to many variants, including
multi-modal NER (MNER) or grounded MNER (GMNER). A key challenge to these tasks
is that the model should be able to generalize to the entities unseen during
the training, and should be able to handle the training samples with noisy
annotations. To address this obstacle, we propose SCANNER (Span CANdidate
detection and recognition for NER), a model capable of effectively handling all
three NER variants. SCANNER is a two-stage structure; we extract entity
candidates in the first stage and use it as a query to get knowledge,
effectively pulling knowledge from various sources. We can boost our
performance by utilizing this entity-centric extracted knowledge to address
unseen entities. Furthermore, to tackle the challenges arising from noisy
annotations in NER datasets, we introduce a novel self-distillation method,
enhancing the robustness and accuracy of our model in processing training data
with inherent uncertainties. Our approach demonstrates competitive performance
on the NER benchmark and surpasses existing methods on both MNER and GMNER
benchmarks. Further analysis shows that the proposed distillation and knowledge
utilization methods improve the performance of our model on various benchmarks.

最近的研究已经将命名实体识别推向较先进阶段，融入了视觉信号，以致产生了多模态命名实体识别（MNER）或基于图像的 MNER（GMNER）的多个变种。我们提出了 SCANNER（用于 NER 的 Span 候选检测和识别），一种能够有效处理这三种 NER 变体的模型。SCANNER 是一个两阶段结构；我们在第一阶段提取实体候选并将其作为查询，从各种来源中获取知识。通过利用这种以实体为中心的提取知识来处理未知实体，我们可以提高性能。此外，为了应对 NER 数据集中由于噪声注释而出现的挑战，我们引入了一种新的自我蒸馏方法，提高了模型在处理具有固有不确定性的训练数据时的鲁棒性和准确性。我们的方法在 NER 基准测试中展示出有竞争力的性能，并在 MNER 和 GMNER 基准测试中超越了现有方法。进一步的分析表明，所提出的蒸馏和知识利用方法改进了我们的模型在各种基准测试中的性能。