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Recently, there has been an increasing interest in building questionanswering (QA) models that reason across multiple modalities, such as text andimages. However, QA using images is often limited to just picking the answerfrom a pre-defined set of options. In addition, images in the real world,especially in news, have objects that are co-referential to the text, withcomplementary information from both modalities. In this paper, we present a newQA evaluation benchmark with 1,384 questions over news articles that requirecross-media grounding of objects in images onto text. Specifically, the taskinvolves multi-hop questions that require reasoning over image-caption pairs toidentify the grounded visual object being referred to and then predicting aspan from the news body text to answer the question. In addition, we introducea novel multimedia data augmentation framework, based on cross-media knowledgeextraction and synthetic question-answer generation, to automatically augmentdata that can provide weak supervision for this task. We evaluate bothpipeline-based and end-to-end pretraining-based multimedia QA models on ourbenchmark, and show that they achieve promising performance, while considerablylagging behind human performance hence leaving large room for future work onthis challenging new task.

MuMuQA: Multimedia Multi-Hop News Question Answering via Cross-Media Knowledge Extraction and Grounding