  Visual Question Answering (VQA) is a challenging task of natural language
processing (NLP) and computer vision (CV), attracting significant attention
from researchers. English is a resource-rich language that has witnessed
various developments in datasets and models for visual question answering.
Visual question answering in other languages also would be developed for
resources and models. In addition, there is no multilingual dataset targeting
the visual content of a particular country with its own objects and cultural
characteristics. To address the weakness, we provide the research community
with a benchmark dataset named EVJVQA, including 33,000+ pairs of
question-answer over three languages: Vietnamese, English, and Japanese, on
approximately 5,000 images taken from Vietnam for evaluating multilingual VQA
systems or models. EVJVQA is used as a benchmark dataset for the challenge of
multilingual visual question answering at the 9th Workshop on Vietnamese
Language and Speech Processing (VLSP 2022). This task attracted 62 participant
teams from various universities and organizations. In this article, we present
details of the organization of the challenge, an overview of the methods
employed by shared-task participants, and the results. The highest performances
are 0.4392 in F1-score and 0.4009 in BLUE on the private test set. The
multilingual QA systems proposed by the top 2 teams use ViT for the pre-trained
vision model and mT5 for the pre-trained language model, a powerful pre-trained
language model based on the transformer architecture. EVJVQA is a challenging
dataset that motivates NLP and CV researchers to further explore the
multilingual models or systems for visual question answering systems. We
released the challenge on the Codalab evaluation system for further research.
