A Parallel Evaluation Data Set of Software Documentation with Document Structure Annotation
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Abstract (arXiv)
This paper accompanies the software documentation data set for machine translation, a parallel evaluation data set of data originating from the SAP Help Portal, that we released to the machine translation community for research purposes. It offers the possibility to tune and evaluate machine translation systems in the domain of corporate software documentation and contributes to the availability of a wider range of evaluation scenarios. The data set comprises of the language pairs English to Hindi, Indonesian, Malay and Thai, and thus also increases the test coverage for the many low-resource language pairs. Unlike most evaluation data sets that consist of plain parallel text, the segments in this data set come with additional metadata that describes structural information of the document context. We provide insights into the origin and creation, the particularities and characteristics of the data set as well as machine translation results.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 11 Aug 2020.
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Citing papers per year: 2023: 1; 2024: 0; 2025: 2; 2026: 1.
4 papers.
- 2023Efficiently Adapting Pretrained Language Models To New Languagescited by 28
- 2025SEA-BED: How Do Embedding Models Represent Southeast Asian Languages?cited by 2
- 2025Structured Document Translation via Format Reinforcement Learningcited by 0
- 2026LocQE: Principled Domain Adaptation for Localisation Quality Estimation by Leveraging Post-Editscited by 0
- 2026LocQE: Principled Domain Adaptation for Localisation Quality Estimation by Leveraging Post-Edits16 Sep 2026
- 2025Structured Document Translation via Format Reinforcement Learning4 Dec 2025
- 2025SEA-BED: How Do Embedding Models Represent Southeast Asian Languages?17 Aug 2025
- 2023Efficiently Adapting Pretrained Language Models To New Languages9 Nov 2023