Linguistic evaluation of German-English Machine Translation using a Test Suite
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Abstract (arXiv)
We present the results of the application of a grammatical test suite for GermanrightarrowEnglish MT on the systems submitted at WMT19, with a detailed analysis for 107 phenomena organized in 14 categories. The systems still translate wrong one out of four test items in average. Low performance is indicated for idioms, modals, pseudo-clefts, multi-word expressions and verb valency. When compared to last year, there has been a improvement of function words, non-verbal agreement and punctuation. More detailed conclusions about particular systems and phenomena are also presented.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 16 Oct 2019.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 1; 2024: 0; 2025: 1.
4 papers.
- 2022Can Transformer be Too Compositional? Analysing Idiom Processing in Neural Machine Translationcited by 28
- 2023Angler: Helping Machine Translation Practitioners Prioritize Model Improvementscited by 12
- 2021GrASP: A Library for Extracting and Exploring Human-Interpretable Textual Patternscited by 6
- 2025The Box is in the Pen: Evaluating Commonsense Reasoning in Neural Machine Translationcited by 5
- 2025The Box is in the Pen: Evaluating Commonsense Reasoning in Neural Machine Translation5 Mar 2025
- 2023Angler: Helping Machine Translation Practitioners Prioritize Model Improvements12 Apr 2023
- 2022Can Transformer be Too Compositional? Analysing Idiom Processing in Neural Machine Translation30 May 2022
- 2021GrASP: A Library for Extracting and Exploring Human-Interpretable Textual Patterns8 Apr 2021