# Video-guided Machine Translation with Global Video Context

arXiv: https://arxiv.org/abs/2604.06789 · HTML: https://mlsanity.com/paper/2604.06789/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 27 of its 35 references that are in the map.
- First posted on arXiv 8 Apr 2026.

## Builds on (most cited first, 27 of 27)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Qwen3 Technical Report](https://mlsanity.com/paper/2505.09388/) (2025)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [Billion-scale similarity search with GPUs](https://mlsanity.com/paper/1702.08734/) (2017)
- [ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks](https://mlsanity.com/paper/1908.02265/) (2019)
- [fairseq: A Fast, Extensible Toolkit for Sequence Modeling](https://mlsanity.com/paper/1904.01038/) (2019)
- [Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation](https://mlsanity.com/paper/1609.08144/) (2016)
- [On the Variance of the Adaptive Learning Rate and Beyond](https://mlsanity.com/paper/1908.03265/) (2019)
- [VATEX: A Large-Scale, High-Quality Multilingual Dataset for Video-and-Language Research](https://mlsanity.com/paper/1904.03493/) (2019)
- [Multi30K: Multilingual English-German Image Descriptions](https://mlsanity.com/paper/1605.00459/) (2016)
- [How2: A Large-scale Dataset for Multimodal Language Understanding](https://mlsanity.com/paper/1811.00347/) (2018)
- [A Novel Graph-based Multi-modal Fusion Encoder for Neural Machine Translation](https://mlsanity.com/paper/2007.08742/) (2020)
- [Doubly-Attentive Decoder for Multi-modal Neural Machine Translation](https://mlsanity.com/paper/1702.01287/) (2017)
- [Incorporating Global Visual Features into Attention-Based Neural Machine Translation](https://mlsanity.com/paper/1701.06521/) (2017)
- [Dynamic Context-guided Capsule Network for Multimodal Machine Translation](https://mlsanity.com/paper/2009.02016/) (2020)
- [On Vision Features in Multimodal Machine Translation](https://mlsanity.com/paper/2203.09173/) (2022)
- [Multimodal Machine Translation through Visuals and Speech](https://mlsanity.com/paper/1911.12798/) (2019)
- [Multimodal Attention for Neural Machine Translation](https://mlsanity.com/paper/1609.03976/) (2016)
- [VISA: An Ambiguous Subtitles Dataset for Visual Scene-Aware Machine Translation](https://mlsanity.com/paper/2201.08054/) (2022)
- [BigVideo: A Large-scale Video Subtitle Translation Dataset for Multimodal Machine Translation](https://mlsanity.com/paper/2305.18326/) (2023)
- [Multilingual Image Description with Neural Sequence Models](https://mlsanity.com/paper/1510.04709/) (2015)
- [Multimodal Neural Machine Translation with Search Engine Based Image Retrieval](https://mlsanity.com/paper/2208.00767/) (2022)
- [Video-Helpful Multimodal Machine Translation](https://mlsanity.com/paper/2310.20201/) (2023)
- [TopicVD: A Topic-Based Dataset of Video-Guided Multimodal Machine Translation for Documentaries](https://mlsanity.com/paper/2505.05714/) (2025)
- [Exploring the Necessity of Visual Modality in Multimodal Machine Translation using Authentic Datasets](https://mlsanity.com/paper/2404.06107/) (2024)

## Abstract (arXiv)

Video-guided Multimodal Translation (VMT) has advanced significantly in recent years. However, most existing methods rely on locally aligned video segments paired one-to-one with subtitles, limiting their ability to capture global narrative context across multiple segments in long videos. To overcome this limitation, we propose a globally video-guided multimodal translation framework that leverages a pretrained semantic encoder and vector database-based subtitle retrieval to construct a context set of video segments closely related to the target subtitle semantics. An attention mechanism is employed to focus on highly relevant visual content, while preserving the remaining video features to retain broader contextual information. Furthermore, we design a region-aware cross-modal attention mechanism to enhance semantic alignment during translation. Experiments on a large-scale documentary translation dataset demonstrate that our method significantly outperforms baseline models, highlighting its effectiveness in long-video scenarios.