Pivoting through a popular language with more parallel corpora available (e.g. English and Chinese) is a common approach to build machine translation (MT) systems for low-resource languages. For example, to build a Russian-to Spanish MT system, we could build one system using the Russian–Spanish corpus directly. We could also build two systems, Russian-to-English and English-to Spanish, as the resources of the two language pairs are much larger than the Russian–Spanish pair, and use them cascadingly to translate texts in Russian into Spanish by pivoting through English. There are, however, some confusing results on the Pivot MT approach in the literature. In this paper, we reviewed the performance of Pivot MT with the United Nations Parallel Corpus v1.0 (UN6Way) using both English and Chinese as pivot languages. We also report our system performance on the CWMT 2018 Pivot MT shared task, where Japanese patent sentences are translated into English using Chinese as the pivot language.