# LIUM-CVC Submissions for WMT17 Multimodal Translation Task

arXiv: https://arxiv.org/abs/1707.04481 · HTML: https://mlsanity.com/paper/1707.04481/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jul 2017.

## Built on by (most cited first, 7 of 7)

- [Neural Machine Translation with Phrase-Level Universal Visual Representations](https://mlsanity.com/paper/2203.10299/) (2022)
- [Gumbel-Attention for Multi-modal Machine Translation](https://mlsanity.com/paper/2103.08862/) (2021)
- [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)
- [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)
- [The Case for Evaluating Multimodal Translation Models on Text Datasets](https://mlsanity.com/paper/2403.03014/) (2024)

## Abstract (arXiv)

This paper describes the monomodal and multimodal Neural Machine Translation systems developed by LIUM and CVC for WMT17 Shared Task on Multimodal Translation. We mainly explored two multimodal architectures where either global visual features or convolutional feature maps are integrated in order to benefit from visual context. Our final systems ranked first for both En-De and En-Fr language pairs according to the automatic evaluation metrics METEOR and BLEU.