Multimodal Entity Linking (MEL) which aims at linking mentions with
multimodal contexts to the referent entities from a knowledge base (e.g.,
Wikipedia), is an essential task for many multimodal applications. Although
much attention has been paid to MEL, the shortcomings of existing MEL datasets
including limited contextual topics and entity types, simplified mention
ambiguity, and restricted availability, have caused great obstacles to the
research and application of MEL. In this paper, we present WikiDiverse, a
high-quality human-annotated MEL dataset with diversified contextual topics and
entity types from Wikinews, which uses Wikipedia as the corresponding knowledge
base. A well-tailored annotation procedure is adopted to ensure the quality of
the dataset. Based on WikiDiverse, a sequence of well-designed MEL models with
intra-modality and inter-modality attentions are implemented, which utilize the
visual information of images more adequately than existing MEL models do.
Extensive experimental analyses are conducted to investigate the contributions
of different modalities in terms of MEL, facilitating the future research on
this task. The dataset and baseline models are available at
this https URL.

本研究提出了 WikiDiverse，一个高质量的人工注释的 Multimodal Entity Linking（MEL）数据集，利用该数据集提出了一系列好的基于 intra-modality 和 inter-modality attentions 的 MEL 模型，比现有的 MEL 模型更充分利用图像的视觉信息，并通过大量实验分析了不同模态在 MEL 任务中的贡献。