Journal of Computer Applications ›› 2023, Vol. 43 ›› Issue (2): 329-334.DOI: 10.11772/j.issn.1001-9081.2021122067

• Artificial intelligence •     Next Articles

Review of interactive machine translation

Xingbin LIAO1,2, Xiaolin QIN1,2(), Siqi ZHANG1,2, Yangge QIAN1,2   

  1. 1.Chengdu Institute of Computer Applications,Chinese Academy of Sciences,Chengdu Sichuan 610213,China
    2.School of Computer Science and Technology,University of Chinese Academy of Sciences,Beijing 101408,China
  • Received:2021-12-09 Revised:2022-05-03 Accepted:2022-06-10 Online:2022-06-30 Published:2023-02-10
  • Contact: Xiaolin QIN
  • About author:LIAO Xingbin, born in 1994, M. S. candidate. His research interests include natural language processing, data leakage prevention.
    QIN Xiaolin, born in 1980, Ph. D., research fellow. His research interests include artificial intelligence, automated reasoning.
    ZHANG Siqi, born in 1995, Ph. D. candidate. His research interests include natural language processing, temporal logic.
    QIAN Yangge, born in 1997, M. S. candidate. His research interests include natural language processing, artificial intelligence.
  • Supported by:
    Sichuan Science and Technology Program(2019ZDZX0006);CAS STS Plan Regional Key Project (Class A)(KFJ?STS?QYZD?2021?21?001)


廖兴滨1,2, 秦小林1,2(), 张思齐1,2, 钱杨舸1,2   

  1. 1.中国科学院 成都计算机应用研究所,成都 610213
    2.中国科学院大学 计算机科学与技术学院,北京 101408
  • 通讯作者: 秦小林
  • 作者简介:廖兴滨(1994—),男,湖北十堰人,硕士研究生,CCF会员,主要研究方向:自然语言处理、数据泄露预防
  • 基金资助:


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.

Key words: machine translation, Interactive Machine Translation (IMT), Interactive Statistical Machine Translation (ISMT), Interactive Neural Machine Translation (INMT), Reinforcement Learning (RL), Natural Language Processing (NLP)



关键词: 机器翻译, 交互式机器翻译, 交互式统计机器翻译, 交互式神经机器翻译, 强化学习, 自然语言处理

CLC Number: