MaXM: Towards Multilingual Visual Question Answering

Soravit Changpinyo, Linting Xue, Michal Yarom, Ashish V Thapliyal, Idan Szpektor, Julien Amelot, Xi Chen, Radu Soricut

Submission Type: Regular Long Paper

Submission Track: Language Grounding to Vision, Robotics and Beyond

Submission Track 2: Multilinguality and Linguistic Diversity

Keywords: VQA, multilinguality

TL;DR: New benchmark and scalable data creation for multilingual visual question answering

Abstract: Visual Question Answering (VQA) has been primarily studied through the lens of the English language. Yet, tackling VQA in other languages in the same manner would require a considerable amount of resources. In this paper, we propose scalable solutions to multilingual visual question answering (mVQA), on both data and modeling fronts. We first propose a translation-based framework to mVQA data generation that requires much less human annotation efforts than the conventional approach of directly collection questions and answers. Then, we apply our framework to the multilingual captions in the Crossmodal-3600 dataset and develop an efficient annotation protocol to create MaXM, a test-only VQA benchmark in 7 diverse languages. Finally, we develop a simple, lightweight, and effective approach as well as benchmark state-of-the-art English and multilingual VQA models. We hope that our benchmark encourages further research on mVQA.

Submission Number: 2552