  With massive explosion of social media such as Twitter and Instagram, people
daily share billions of multimedia posts, containing images and text.
Typically, text in these posts is short, informal and noisy, leading to
ambiguities which can be resolved using images. In this paper we explore
text-centric Named Entity Recognition task on these multimedia posts. We
propose an end to end model which learns a joint representation of a text and
an image. Our model extends multi-dimensional self attention technique, where
now image helps to enhance relationship between words. Experiments show that
our model is capable of capturing both textual and visual contexts with greater
accuracy, achieving state-of-the-art results on Twitter multimodal Named Entity
Recognition dataset.
