Building a reliable visual question answering~(VQA) system across different
languages is a challenging problem, primarily due to the lack of abundant
samples for training. To address this challenge, recent studies have employed
machine translation systems for the cross-lingual VQA task. This involves
translating the evaluation samples into a source language (usually English) and
using monolingual models (i.e., translate-test). However, our analysis reveals
that translated texts contain unique characteristics distinct from
human-written ones, referred to as translation artifacts. We find that these
artifacts can significantly affect the models, confirmed by extensive
experiments across diverse models, languages, and translation processes. In
light of this, we present a simple data augmentation strategy that can
alleviate the adverse impacts of translation artifacts.

建立一个可靠的跨语言视觉问答系统是一个具有挑战性的问题，主要原因是在训练过程中缺乏丰富的样本。为了解决这一问题，最近的研究采用了机器翻译系统进行跨语言视觉问答任务。然而，我们的分析发现翻译文本具有独特的特征，与人工编写的文本不同，被称为翻译人工痕迹。我们发现这些痕迹可以对模型产生显著影响，通过在不同的模型、语言和翻译过程上进行广泛实验证实了这一点。鉴于此，我们提出了一种简单的数据增强策略，可以缓解翻译人工痕迹的不利影响。