# UMONS Submission for WMT18 Multimodal Translation Task

arXiv: https://arxiv.org/abs/1810.06233 · HTML: https://mlsanity.com/paper/1810.06233/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Oct 2018.

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

- [Cross-lingual Visual Pre-training for Multimodal Machine Translation](https://mlsanity.com/paper/2101.10044/) (2021)
- [Deep soccer captioning with transformer: dataset, semantics-related losses, and multi-level evaluation](https://mlsanity.com/paper/2202.05728/) (2022)

## Abstract (arXiv)

This paper describes the UMONS solution for the Multimodal Machine Translation Task presented at the third conference on machine translation (WMT18). We explore a novel architecture, called deepGRU, based on recent findings in the related task of Neural Image Captioning (NIC). The models presented in the following sections lead to the best METEOR translation score for both constrained (English, image) -> German and (English, image) -> French sub-tasks.