# Fine-grained evaluation of German-English Machine Translation based on a Test Suite

arXiv: https://arxiv.org/abs/1910.07460 · HTML: https://mlsanity.com/paper/1910.07460/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Oct 2019.

## Built on by (most cited first, 6 of 6)

- [Angler: Helping Machine Translation Practitioners Prioritize Model Improvements](https://mlsanity.com/paper/2304.05967/) (2023)
- [GrASP: A Library for Extracting and Exploring Human-Interpretable Textual Patterns](https://mlsanity.com/paper/2104.03958/) (2021)
- [ComSum: Commit Messages Summarization and Meaning Preservation](https://mlsanity.com/paper/2108.10763/) (2021)
- [Augmenting Text to Increase Translation Difficulty](https://mlsanity.com/paper/2608.15932/) (2026)
- [MuLER: Detailed and Scalable Reference-based Evaluation](https://mlsanity.com/paper/2305.14991/) (2023)
- [Bidirectional Chinese and English Passive Sentences Dataset for Machine Translation](https://mlsanity.com/paper/2603.15227/) (2026)

## Abstract (arXiv)

We present an analysis of 16 state-of-the-art MT systems on German-English based on a linguistically-motivated test suite. The test suite has been devised manually by a team of language professionals in order to cover a broad variety of linguistic phenomena that MT often fails to translate properly. It contains 5,000 test sentences covering 106 linguistic phenomena in 14 categories, with an increased focus on verb tenses, aspects and moods. The MT outputs are evaluated in a semi-automatic way through regular expressions that focus only on the part of the sentence that is relevant to each phenomenon. Through our analysis, we are able to compare systems based on their performance on these categories. Additionally, we reveal strengths and weaknesses of particular systems and we identify grammatical phenomena where the overall performance of MT is relatively low.