Cross-Media Keyphrase Prediction: A Unified Framework with Multi-Modality Multi-Head Attention and Image Wordings

Yue Wang, Jing Li, Michael R. Lyu, Irwin King

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review


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 MultiHead 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 mechanisms. Further analyses show that our multi-head attention is able to attend information from various aspects and boost classification or generation in diverse scenarios.
Original languageEnglish
Title of host publication2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Number of pages14
Publication statusPublished - Nov 2021

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