# CUNI System for the WMT19 Robustness Task

arXiv: https://arxiv.org/abs/1906.09246 · HTML: https://mlsanity.com/paper/1906.09246/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jun 2019.

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

- [Addressing the Vulnerability of NMT in Input Perturbations](https://mlsanity.com/paper/2104.09810/) (2021)
- [Impact of Visual Context on Noisy Multimodal NMT: An Empirical Study for English to Indian Languages](https://mlsanity.com/paper/2308.16075/) (2023)

## Abstract (arXiv)

We present our submission to the WMT19 Robustness Task. Our baseline system is the Charles University (CUNI) Transformer system trained for the WMT18 shared task on News Translation. Quantitative results show that the CUNI Transformer system is already far more robust to noisy input than the LSTM-based baseline provided by the task organizers. We further improved the performance of our model by fine-tuning on the in-domain noisy data without influencing the translation quality on the news domain.