  Multi-modal named entity recognition (MNER) aims at identifying entity spans
and recognizing their categories in social media posts with the aid of images.
However, in dominant MNER approaches, the interaction of different modalities
is usually carried out through the alternation of self-attention and
cross-attention or over-reliance on the gating machine, which results in
imprecise and biased correspondence between fine-grained semantic units of text
and image. To address this issue, we propose a Flat Multi-modal Interaction
Transformer (FMIT) for MNER. Specifically, we first utilize noun phrases in
sentences and general domain words to obtain visual cues. Then, we transform
the fine-grained semantic representation of the vision and text into a unified
lattice structure and design a novel relative position encoding to match
different modalities in Transformer. Meanwhile, we propose to leverage entity
boundary detection as an auxiliary task to alleviate visual bias. Experiments
show that our methods achieve the new state-of-the-art performance on two
benchmark datasets.
