Benchmarking Diverse-Modal Entity Linking with Generative Models
Sijia Wang Alexander Hanbo Li Henry Zhu Sheng Zhang Chung-Wei Hang Pramuditha Perera Jie Ma William Wang Zhiguo Wang Vittorio Castelli Bing Xiang Patrick Ng
Entities can be expressed in diverse formats, such as texts, images, or
column names and cell values in tables. While existing entity linking (EL)
models work well on per modality configuration, such as text-only EL, visual
grounding, or schema linking, it is more challenging to design a unified model
for diverse modality configurations. To bring various modality configurations
together, we constructed a benchmark for diverse-modal EL (DMEL) from existing
EL datasets, covering all three modalities including text, image, and table. To
approach the DMEL task, we proposed a generative diverse-modal model (GDMM)
following a multimodal-encoder-decoder paradigm. Pre-training \Model with rich
corpora builds a solid foundation for DMEL without storing the entire KB for
inference. Fine-tuning GDMM builds a stronger DMEL baseline, outperforming
state-of-the-art task-specific EL models by 8.51 F1 score on average.
Additionally, extensive error analyses are conducted to highlight the
challenges of DMEL, facilitating future research on this task.