With the development and maturity of deep learning， the quality of neural machine translation has increased， yet it is still not perfect and requires human post-editing to achieve acceptable translation results. Interactive Machine Translation （IMT） is an alternative to this serial work， that is performing human interaction during the translation process， where the user verifies the candidate translations produced by the translation system and， if necessary， provides new input， and the system generates new candidate translations based on the current feedback of users， this process repeats until a satisfactory output is produced. Firstly， the basic concept and the current research progresses of IMT were introduced. Then， some common methods and state-of-the-art works were suggested in classification， while the background and innovation of each work were briefly described. Finally， the development trends and research difficulties of IMT were discussed.