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.

翻译：视觉问答（VQA）是自然语言处理（NLP）和计算机视觉（CV）领域的一项具有挑战性的任务，吸引了研究者的广泛关注。英语作为资源丰富的语言，在视觉问答的数据集和模型方面已取得多项进展。其他语言的视觉问答也需要相应的资源与模型来发展。此外，目前尚缺乏针对特定国家视觉内容（包含其特有物体与文化特征）的多语言数据集。为解决这一不足，我们向研究社区提供了一个名为EVJVQA的基准数据集，包含基于约5000张越南拍摄图像的33000多组问答对，涵盖越南语、英语和日语三种语言，用于评估多语言VQA系统或模型。EVJVQA被用作第9届越南语言与语音处理研讨会（VLSP 2022）多语言视觉问答挑战赛的基准数据集。该任务吸引了来自不同大学和机构的62支参赛队伍。本文详细介绍了挑战赛的组织情况、参赛队伍使用的方法概述以及结果。在私有测试集上，最佳性能分别达到F1分数0.4392和BLEU分数0.4009。前两名团队提出的多语言问答系统采用ViT作为预训练视觉模型，mT5作为预训练语言模型（一种基于Transformer架构的强大预训练语言模型）。EVJVQA作为具有挑战性的数据集，将激励NLP及CV研究者进一步探索面向视觉问答系统的多语言模型或系统。