Paper Info
Title
MuMuQA: Multimedia Multi-Hop News Question Answering via Cross-Media Knowledge Extraction and Grounding.
Abstract
Recently, there has been an increasing interest in building question answering (QA) models that reason across multiple modalities, such as text and images. However, QA using images is often limited to just picking the answer from 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, with complementary information from both modalities. In this paper, we present a new QA evaluation benchmark with 1,384 questions over news articles that require cross-media grounding of objects in images onto text. Specifically, the task involves multi-hop questions that require reasoning over image-caption pairs to identify the grounded visual object being referred to and then predicting a span from the news body text to answer the question. In addition, we introduce a novel multimedia data augmentation framework, based on cross-media knowledge extraction and synthetic question-answer generation, to automatically augment data that can provide weak supervision for this task. We evaluate both pipeline-based and end-to-end pretraining-based multimedia QA models on our benchmark, and show that they achieve promising performance, while considerably lagging behind human performance hence leaving large room for future work on this challenging new task.
Year
Venue
Keywords
2022
AAAI Conference on Artificial Intelligence
Speech & Natural Language Processing (SNLP)
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
0
12
Name
Order
Citations
PageRank
Revanth Gangi Reddy100.68
Xilin Rui200.34
Manling Li387.89
Xudong Lin452.77
Haoyang Wen552.49
Jaemin Cho671.45
Lifu Huang78112.39
Mohit Bansal887163.19
Avirup Sil913113.85
Shih-Fu Chang10130151101.53
Alexander G. Schwing1169651.78
Heng Ji121544127.27