Translating Headers of Tabular Data: A Pilot Study of Schema Translation
Kunrui Zhu, Yan Gao, Jiaqi Guo, Jian–Guang Lou · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · 2021
Schema translation is the task of automatically translating headers of tabular data from one language to another.High-quality schema translation plays an important role in crosslingual table searching, understanding and analysis.Despite its importance, schema translation is not well studied in the community, and state-of-the-art neural machine translation models cannot work well on this task because of two intrinsic differences between plain text and tabular data: morphological difference and context difference.To facilitate the research study, we construct the first parallel dataset for schema translation, which consists of 3,158 tables with 11,979 headers written in 6 different languages, including English, Chinese, French, German, Spanish, and Japanese.Also, we propose the first schema translation model called CAST, which is a header-to-header neural machine translation model augmented with schema context.Specifically, we model a target header and its context as a directed graph to represent their entity types and relations.Then CAST encodes the graph with a relational-aware transformer and uses another transformer to decode the header in the target language.Experiments on our dataset demonstrate that CAST significantly outperforms state-of-the-art neural machine translation models.Our dataset will be released at https://github.com/microsoft/ContextualSP.