Fine-grained evaluation of German-English Machine Translation based on a Test Suite
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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.
Key facts. Cited by 6 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: 2; 2022: 0; 2023: 2; 2024: 0; 2025: 0; 2026: 2.
6 papers.
- 2023Angler: Helping Machine Translation Practitioners Prioritize Model Improvementscited by 12
- 2021GrASP: A Library for Extracting and Exploring Human-Interpretable Textual Patternscited by 6
- 2021ComSum: Commit Messages Summarization and Meaning Preservationcited by 3
- 2026Augmenting Text to Increase Translation Difficultycited by 1
- 2023MuLER: Detailed and Scalable Reference-based Evaluationcited by 0
- 2026Bidirectional Chinese and English Passive Sentences Dataset for Machine Translationcited by 0
- 2026Augmenting Text to Increase Translation Difficulty16 Aug 2026
- 2026Bidirectional Chinese and English Passive Sentences Dataset for Machine Translation16 Mar 2026
- 2023MuLER: Detailed and Scalable Reference-based Evaluation24 May 2023
- 2023Angler: Helping Machine Translation Practitioners Prioritize Model Improvements12 Apr 2023
- 2021ComSum: Commit Messages Summarization and Meaning Preservation23 Aug 2021
- 2021GrASP: A Library for Extracting and Exploring Human-Interpretable Textual Patterns8 Apr 2021