In this paper, we describe our supervised neural machine translation (NMT) systems that we developed for the news translation task for Kazakh↔ English, Gujarati↔ English, Chinese↔ English, and English→ Finnish translation directions. We focused on leveraging multilingual transfer learning and back-translation for the extremely low-resource language pairs: Kazakh↔ English and Gujarati↔ English translation. For the Chinese↔ English translation, we used the provided parallel data augmented with a large quantity of back-translated monolingual data to train state-of-the-art NMT systems. We then employed techniques that have been proven to be most effective, such as back-translation, fine-tuning, and model ensembling, to generate the primary submissions of Chinese↔ English. For English→ Finnish, our submission from WMT18 remains a strong baseline despite the increase in parallel corpora for this year’s task.