# Microsoft's Submission to the WMT2018 News Translation Task: How I Learned to Stop Worrying and Love the Data

arXiv: https://arxiv.org/abs/1809.00196 · HTML: https://mlsanity.com/paper/1809.00196/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Sep 2018.

## Built on by (most cited first, 5 of 5)

- [Scaling Up Influence Functions](https://mlsanity.com/paper/2112.03052/) (2021)
- [Domain Adaptation and Multi-Domain Adaptation for Neural Machine Translation: A Survey](https://mlsanity.com/paper/2104.06951/) (2021)
- [There's no Data Like Better Data: Using QE Metrics for MT Data Filtering](https://mlsanity.com/paper/2311.05350/) (2023)
- [Domain Adaptation of Machine Translation with Crowdworkers](https://mlsanity.com/paper/2210.15861/) (2022)
- [Separating the Wheat from the Chaff with BREAD: An open-source benchmark and metrics to detect redundancy in text](https://mlsanity.com/paper/2311.06440/) (2023)

## Abstract (arXiv)

This paper describes the Microsoft submission to the WMT2018 news translation shared task. We participated in one language direction -- English-German. Our system follows current best-practice and combines state-of-the-art models with new data filtering (dual conditional cross-entropy filtering) and sentence weighting methods. We trained fairly standard Transformer-big models with an updated version of Edinburgh's training scheme for WMT2017 and experimented with different filtering schemes for Paracrawl. According to automatic metrics (BLEU) we reached the highest score for this subtask with a nearly 2 BLEU point margin over the next strongest system. Based on human evaluation we ranked first among constrained systems. We believe this is mostly caused by our data filtering/weighting regime.