# CUNI System for WMT16 Automatic Post-Editing and Multimodal Translation Tasks

arXiv: https://arxiv.org/abs/1606.07481 · HTML: https://mlsanity.com/paper/1606.07481/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jun 2016.

## Built on by (most cited first, 4 of 4)

- [A Survey on Low-Resource Neural Machine Translation](https://mlsanity.com/paper/2107.04239/) (2021)
- [Towards Zero-Shot Multimodal Machine Translation](https://mlsanity.com/paper/2407.13579/) (2024)
- [Bilingual Synchronization: Restoring Translational Relationships with Editing Operations](https://mlsanity.com/paper/2210.13163/) (2022)
- [Multimodal Machine Translation with Visual Scene Graph Pruning](https://mlsanity.com/paper/2505.19507/) (2025)

## Abstract (arXiv)

Neural sequence to sequence learning recently became a very promising paradigm in machine translation, achieving competitive results with statistical phrase-based systems. In this system description paper, we attempt to utilize several recently published methods used for neural sequential learning in order to build systems for WMT 2016 shared tasks of Automatic Post-Editing and Multimodal Machine Translation.