AMELI: Enhancing Multimodal Entity Linking with Fine-Grained Attributes
Barry Menglong Yao Yu Chen Qifan Wang Sijia Wang Minqian Liu Zhiyang Xu Licheng Yu Lifu Huang
We propose attribute-aware multimodal entity linking, where the input is a
mention described with a text and image, and the goal is to predict the
corresponding target entity from a multimodal knowledge base (KB) where each
entity is also described with a text description, a visual image and a set of
attributes and values. To support this research, we construct AMELI, a
large-scale dataset consisting of 18,472 reviews and 35,598 products. To
establish baseline performance on AMELI, we experiment with the current
state-of-the-art multimodal entity linking approaches and our enhanced
attribute-aware model and demonstrate the importance of incorporating the
attribute information into the entity linking process. To be best of our
knowledge, we are the first to build benchmark dataset and solutions for the
attribute-aware multimodal entity linking task. Datasets and codes will be made
publicly available.