Learning Implicit Entity-object Relations by Bidirectional Generative
  Alignment for Multimodal NER
Feng Chen Jiajia Liu Kaixiang Ji Wang Ren Jian Wang Jingdong Wang
The challenge posed by multimodal named entity recognition (MNER) is mainly
two-fold: (1) bridging the semantic gap between text and image and (2) matching
the entity with its associated object in image. Existing methods fail to
capture the implicit entity-object relations, due to the lack of corresponding
annotation. In this paper, we propose a bidirectional generative alignment
method named BGA-MNER to tackle these issues. Our BGA-MNER consists of
\texttt{image2text} and \texttt{text2image} generation with respect to
entity-salient content in two modalities. It jointly optimizes the
bidirectional reconstruction objectives, leading to aligning the implicit
entity-object relations under such direct and powerful constraints.
Furthermore, image-text pairs usually contain unmatched components which are
noisy for generation. A stage-refined context sampler is proposed to extract
the matched cross-modal content for generation. Extensive experiments on two
benchmarks demonstrate that our method achieves state-of-the-art performance
without image input during inference.