# A High-Quality Multilingual Dataset for Structured Documentation Translation

arXiv: https://arxiv.org/abs/2006.13425 · HTML: https://mlsanity.com/paper/2006.13425/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jun 2020.

## Built on by (most cited first, 10 of 10)

- [WSPAlign: Word Alignment Pre-training via Large-Scale Weakly Supervised Span Prediction](https://mlsanity.com/paper/2306.05644/) (2023)
- [Deep Indexed Active Learning for Matching Heterogeneous Entity Representations](https://mlsanity.com/paper/2104.03986/) (2021)
- [Beyond Decoder-only: Large Language Models Can be Good Encoders for Machine Translation](https://mlsanity.com/paper/2503.06594/) (2025)
- [Take One Step at a Time to Know Incremental Utility of Demonstration: An Analysis on Reranking for Few-Shot In-Context Learning](https://mlsanity.com/paper/2311.09619/) (2023)
- [OmniAlign: A Unified Multilingual Aligner for Word and Sentence Alignment](https://mlsanity.com/paper/2608.18474/) (2026)
- [A Template-based Method for Constrained Neural Machine Translation](https://mlsanity.com/paper/2205.11255/) (2022)
- [Reference Trustable Decoding: A Training-Free Augmentation Paradigm for Large Language Models](https://mlsanity.com/paper/2409.20181/) (2024)
- [Structured Document Translation via Format Reinforcement Learning](https://mlsanity.com/paper/2512.05100/) (2025)
- [Just Use XML: Revisiting Joint Translation and Label Projection](https://mlsanity.com/paper/2603.12021/) (2026)
- [CzechDocs: A Multiway Parallel Dataset of Formatted Documents for Minority Languages in Czechia](https://mlsanity.com/paper/2606.20212/) (2026)

## Abstract (arXiv)

This paper presents a high-quality multilingual dataset for the documentation domain to advance research on localization of structured text. Unlike widely-used datasets for translation of plain text, we collect XML-structured parallel text segments from the online documentation for an enterprise software platform. These Web pages have been professionally translated from English into 16 languages and maintained by domain experts, and around 100,000 text segments are available for each language pair. We build and evaluate translation models for seven target languages from English, with several different copy mechanisms and an XML-constrained beam search. We also experiment with a non-English pair to show that our dataset has the potential to explicitly enable 17 × 16 translation settings. Our experiments show that learning to translate with the XML tags improves translation accuracy, and the beam search accurately generates XML structures. We also discuss trade-offs of using the copy mechanisms by focusing on translation of numerical words and named entities. We further provide a detailed human analysis of gaps between the model output and human translations for real-world applications, including suitability for post-editing.