In recent years, multi-modal machine translation has attracted significant
interest in both academia and industry due to its superior performance. It
takes both textual and visual modalities as inputs, leveraging visual context
to tackle the ambiguities in source texts. In this paper, we begin by offering
an exhaustive overview of 99 prior works, comprehensively summarizing
representative studies from the perspectives of dominant models, datasets, and
evaluation metrics. Afterwards, we analyze the impact of various factors on
model performance and finally discuss the possible research directions for this
task in the future. Over time, multi-modal machine translation has developed
more types to meet diverse needs. Unlike previous surveys confined to the early
stage of multi-modal machine translation, our survey thoroughly concludes these
emerging types from different aspects, so as to provide researchers with a
better understanding of its current state.

多模态机器翻译是近年来引起学术界和工业界广泛关注的研究领域，本文通过综述先前的 99 项研究工作，全面总结了主要模型、数据集和评估指标，分析了各种因素对模型性能的影响，并讨论了未来该领域的研究方向。与之前限制在早期多模态机器翻译的调查不同，我们的调查从不同角度深入总结了这些新兴类型，以便为研究人员提供对目前研究状况的更好理解。