Visual features encompass visual information extracted from images or videos, serving as
supplementary input to enhance the efficacy of neural machine translation (NMT) systems …

Abstract Domain-specific Multi-modal Neural Machine Translation (DMNMT) aims to
translate domain-specific sentences from a source language to a target language by …

The goal of multi-modal neural machine translation (MNMT) is to incorporate language-
agnostic visual information into text to enhance the performance of machine translation …

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 …

We present a large-scale video subtitle translation dataset, BigVideo, to facilitate the study of
multi-modality machine translation. Compared with the widely used How2 and VaTeX …

Controlling styles in neural machine translation (NMT) has attracted wide attention, as it is
crucial for enhancing user experience. Earlier studies on this topic typically concentrate on …

Multi-modal machine translation (MMT) can reduce ambiguity and semantic distortion
compared with traditional machine translation (MT) by utilizing auxiliary information such as …

Incorporating domain-specific visual information into text poses one of the critical challenges
for domain-specific multi-modal neural machine translation (DMNMT). While most existing …

Current video-guided machine translation (VMT) approaches primarily use coarse-grained
visual information, resulting in information redundancy, high computational overhead, and …

Visual information has been introduced for enhancing machine translation (MT), and its
effectiveness heavily relies on the availability of large amounts of bilingual parallel sentence …