# CUNI System for the WMT18 Multimodal Translation Task

arXiv: https://arxiv.org/abs/1811.04697 · HTML: https://mlsanity.com/paper/1811.04697/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Nov 2018.

## Built on by (most cited first, 13 of 13)

- [Good for Misconceived Reasons: An Empirical Revisiting on the Need for Visual Context in Multimodal Machine Translation](https://mlsanity.com/paper/2105.14462/) (2021)
- [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)
- [Searchable Hidden Intermediates for End-to-End Models of Decomposable Sequence Tasks](https://mlsanity.com/paper/2105.00573/) (2021)
- [Vision Matters When It Should: Sanity Checking Multimodal Machine Translation Models](https://mlsanity.com/paper/2109.03415/) (2021)
- [LVP-M3: Language-aware Visual Prompt for Multilingual Multimodal Machine Translation](https://mlsanity.com/paper/2210.15461/) (2022)
- [Cross-lingual Visual Pre-training for Multimodal Machine Translation](https://mlsanity.com/paper/2101.10044/) (2021)
- [Distill the Image to Nowhere: Inversion Knowledge Distillation for Multimodal Machine Translation](https://mlsanity.com/paper/2210.04468/) (2022)
- [Increasing Visual Awareness in Multimodal Neural Machine Translation from an Information Theoretic Perspective](https://mlsanity.com/paper/2210.08478/) (2022)
- [The JDDC 2.0 Corpus: A Large-Scale Multimodal Multi-Turn Chinese Dialogue Dataset for E-commerce Customer Service](https://mlsanity.com/paper/2109.12913/) (2021)
- [Low-resource Neural Machine Translation with Cross-modal Alignment](https://mlsanity.com/paper/2210.06716/) (2022)
- [Semi-Autoregressive Streaming ASR With Label Context](https://mlsanity.com/paper/2309.10926/) (2023)
- [Dual-branch Prompting for Multimodal Machine Translation](https://mlsanity.com/paper/2507.17588/) (2025)

## Abstract (arXiv)

We present our submission to the WMT18 Multimodal Translation Task. The main feature of our submission is applying a self-attentive network instead of a recurrent neural network. We evaluate two methods of incorporating the visual features in the model: first, we include the image representation as another input to the network; second, we train the model to predict the visual features and use it as an auxiliary objective. For our submission, we acquired both textual and multimodal additional data. Both of the proposed methods yield significant improvements over recurrent networks and self-attentive textual baselines.