We propose a touch-based editing method for translation, which is more flexible than tradi- tional keyboard-mouse-based translation post- editing. This approach relies on touch actions that users perform to indicate translation errors. We present a dual-encoder model to handle the actions and generate refined translations. To mimic the user feedback, we adopt the TER al- gorithm comparing between draft translations and references to automatically extract the sim- ulated actions for training data construction. Experiments on translation datasets with sim- ulated editing actions show that our method significantly improves original translation of Transformer (up to 25.31 BLEU) and outper- forms existing interactive translation methods (up to 16.64 BLEU). We also conduct ex- periments on post-editing dataset to further prove the robustness and effectiveness of our method.