Program translation is necessary in many real-world scenarios, such as porting codebases from an obsolete or deprecated language to a modern one or re-implementing existing projects in one’s preferred programming language. One way to automate program translation is to make use of Big Code. Existing data-driven approaches either training a translation model or leveraging cross-language retrieval. The former requires large amounts of training data and extra information or neglects significant characteristics of programs. The latter has a barrier to finding the translation with only the features of the input program as the query. In this paper, we present BigPT for interactive cross-language retrieval from Big Code only based on raw code and reusing the retrieved code to assist program translation. We build on existing work on cross-language code representation and we propose a novel predictive transformation model based on auto-encoders. The model is trained on Big Code to generate a target-language representation, which will be used as the query to retrieve the most relevant translations for a given program. Our succinct query enables the user to easily update and correct the returned results to improve the retrieval process. Our experiments show that BigPT outperforms state-of-the-art baselines in terms of program accuracy. Using our novel querying and retrieving mechanism, BigPT can be scaled to the large dataset and efficiently retrieve the translation.