Deep learning-based techniques have been widely applied to the program analysis tasks, in fields such as type inference, fault localization, and code summarization. Hitherto deep learning-based software engineering systems rely thoroughly on supervised learning approaches, which require laborious manual effort to collect and label a prohibitively large amount of data. However, most Turing-complete imperative languages share similar control- and data-flow structures, which make it possible to transfer knowledge learned from one language to another. In this paper, we propose a general cross-lingual transfer learning framework PLATO for program analysis by using a series of techniques that are general to different downstream tasks. PLATO allows Bert-based models to leverage prior knowledge learned from the labeled dataset of one language and transfer it to the others. We evaluate our approaches on several downstream tasks such as type inference and code summarization to demonstrate its feasibility.