The World Wide Web has become a popular source for gathering information and news. Multimodal information, e.g., enriching text with photos, is typically used to convey the news more effectively or to attract attention. The photos can be decorative, depict additional details, or even contain misleading information. Quantifying the cross-modal consistency of entity representations can assist human assessors in evaluating the overall multimodal message. In some cases such measures might give hints to detect fake news, which is an increasingly important topic in today's society. In this paper, we present a multimodal approach to quantify the entity coherence between image and text in real-world news. Named entity linking is applied to extract persons, locations, and events from news texts. Several measures are suggested to calculate the cross-modal similarity of these entities with the news photo, using state-of-the-art computer vision approaches. In contrast to previous work, our system automatically gathers example data from the Web and is applicable to real-world news. The feasibility is demonstrated on two novel datasets that cover different languages, topics, and domains.

Word embeddings, trained through models like skip-gram, have shown to be prone to capturing the biases from the training corpus, e.g. gender bias. Such biases are unwanted as they spill in downstream tasks, thus, leading to discriminatory behavior.