  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 $17 \times 16$
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.
