  Social media produces large amounts of contents every day. To help users
quickly capture what they need, keyphrase prediction is receiving a growing
attention. Nevertheless, most prior efforts focus on text modeling, largely
ignoring the rich features embedded in the matching images. In this work, we
explore the joint effects of texts and images in predicting the keyphrases for
a multimedia post. To better align social media style texts and images, we
propose: (1) a novel Multi-Modality Multi-Head Attention (M3H-Att) to capture
the intricate cross-media interactions; (2) image wordings, in forms of optical
characters and image attributes, to bridge the two modalities. Moreover, we
design a unified framework to leverage the outputs of keyphrase classification
and generation and couple their advantages. Extensive experiments on a
large-scale dataset newly collected from Twitter show that our model
significantly outperforms the previous state of the art based on traditional
attention networks. Further analyses show that our multi-head attention is able
to attend information from various aspects and boost classification or
generation in diverse scenarios.
