Intermediate-Task Training on Multimodal Reasoning Datasets for Zero-Shot Cross-Lingual Visual Question Answering

Abstract

Abstract: Visual question answering (VQA) is one of the crucial vision-and-language tasks. Yet, existing VQA research has mostly focused on the English language, due to a lack of suitable evaluation resources. Previous work on cross-lingual VQA has reported poor zero-shot transfer performance of current multilingual multimodal Transformers with large gaps to monolingual performance, without any deeper analysis. In this work, we delve deeper into the different aspects of cross-lingual VQA, aiming to understand the impact of 1) modeling methods and choices, including architecture, inductive bias, fine-tun

Research Question

To what extent does intermediate-task training on multimodal reasoning datasets improve zero-shot cross-lingual transfer on the visual question answering subset of XTREME-R relative to monolingual English intermediate training?

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