The natural language instructions scripted on the review comments are enormous sources of information about code bug’s nature and expected solutions. In this study, we investigate the performance improvement of repair techniques using code review comments. We train a sequence-to-sequence model on 55,060 code reviews and associated code changes. We also introduce new tokenization and preprocessing approaches that help to achieve significant improvement over state-of-the-art learning-based repair techniques. We boost the top-1 accuracy by 20.33% and top-10 accuracy by 34.82%. We could provide a suggestion for stylistics and non-code errors unaddressed by prior techniques.