# Dual-branch Prompting for Multimodal Machine Translation

arXiv: https://arxiv.org/abs/2507.17588 · HTML: https://mlsanity.com/paper/2507.17588/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 40 of its 78 references that are in the map.
- First posted on arXiv 23 Jul 2025.

## Builds on (most cited first, 30 of 40)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015)
- [fairseq: A Fast, Extensible Toolkit for Sequence Modeling](https://mlsanity.com/paper/1904.01038/) (2019)
- [Latte: Latent Diffusion Transformer for Video Generation](https://mlsanity.com/paper/2401.03048/) (2024)
- [Multi30K: Multilingual English-German Image Descriptions](https://mlsanity.com/paper/1605.00459/) (2016)
- [Deep Dense Multi-scale Network for Snow Removal Using Semantic and Geometric Priors](https://mlsanity.com/paper/2103.11298/) (2021)
- [A Broad Study on the Transferability of Visual Representations with Contrastive Learning](https://mlsanity.com/paper/2103.13517/) (2021)
- [Findings of the Second Shared Task on Multimodal Machine Translation and Multilingual Image Description](https://mlsanity.com/paper/1710.07177/) (2017)
- [Text-Guided Synthesis of Artistic Images with Retrieval-Augmented Diffusion Models](https://mlsanity.com/paper/2207.13038/) (2022)
- [DePT: Decoupled Prompt Tuning](https://mlsanity.com/paper/2309.07439/) (2023)
- [Probing the Need for Visual Context in Multimodal Machine Translation](https://mlsanity.com/paper/1903.08678/) (2019)
- [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)
- [Good for Misconceived Reasons: An Empirical Revisiting on the Need for Visual Context in Multimodal Machine Translation](https://mlsanity.com/paper/2105.14462/) (2021)
- [Incorporating Global Visual Features into Attention-Based Neural Machine Translation](https://mlsanity.com/paper/1701.06521/) (2017)
- [Imagination improves Multimodal Translation](https://mlsanity.com/paper/1705.04350/) (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)
- [Distilling Translations with Visual Awareness](https://mlsanity.com/paper/1906.07701/) (2019)
- [VALHALLA: Visual Hallucination for Machine Translation](https://mlsanity.com/paper/2206.00100/) (2022)
- [Scene Graph as Pivoting: Inference-time Image-free Unsupervised Multimodal Machine Translation with Visual Scene Hallucination](https://mlsanity.com/paper/2305.12256/) (2023)
- [Neural Machine Translation with Phrase-Level Universal Visual Representations](https://mlsanity.com/paper/2203.10299/) (2022)
- [Tackling Ambiguity with Images: Improved Multimodal Machine Translation and Contrastive Evaluation](https://mlsanity.com/paper/2212.10140/) (2022)
- [Label2Label: A Language Modeling Framework for Multi-Attribute Learning](https://mlsanity.com/paper/2207.08677/) (2022)
- [Generative Imagination Elevates Machine Translation](https://mlsanity.com/paper/2009.09654/) (2020)
- [Gumbel-Attention for Multi-modal Machine Translation](https://mlsanity.com/paper/2103.08862/) (2021)
- [Reliable Few-shot Learning under Dual Noises](https://mlsanity.com/paper/2506.16330/) (2025)
- [CUNI System for the WMT18 Multimodal Translation Task](https://mlsanity.com/paper/1811.04697/) (2018)

## Built on by (most cited first, 2 of 2)

- [Benchmarking Few-shot Transferability of Pre-trained Models with Improved Evaluation Protocols](https://mlsanity.com/paper/2603.00478/) (2026)
- [Structure-aware Prompt Adaptation from Seen to Unseen for Open-Vocabulary Compositional Zero-Shot Learning](https://mlsanity.com/paper/2603.03815/) (2026)

## Abstract (arXiv)

Multimodal Machine Translation (MMT) typically enhances text-only translation by incorporating aligned visual features. Despite the remarkable progress, state-of-the-art MMT approaches often rely on paired image-text inputs at inference and are sensitive to irrelevant visual noise, which limits their robustness and practical applicability. To address these issues, we propose D2P-MMT, a diffusion-based dual-branch prompting framework for robust vision-guided translation. Specifically, D2P-MMT requires only the source text and a reconstructed image generated by a pre-trained diffusion model, which naturally filters out distracting visual details while preserving semantic cues. During training, the model jointly learns from both authentic and reconstructed images using a dual-branch prompting strategy, encouraging rich cross-modal interactions. To bridge the modality gap and mitigate training-inference discrepancies, we introduce a distributional alignment loss that enforces consistency between the output distributions of the two branches. Extensive experiments on the Multi30K dataset demonstrate that D2P-MMT achieves superior translation performance compared to existing state-of-the-art approaches. Our code is publicly available at https://github.com/MentaY/DDP.