@inproceedings{li-etal-2021-miss,
    title = "{M}i{SS}: An Assistant for Multi-Style Simultaneous Translation",
    author = "Li, Zuchao  and
      Parnow, Kevin  and
      Utiyama, Masao  and
      Sumita, Eiichiro  and
      Zhao, Hai",
    editor = "Adel, Heike  and
      Shi, Shuming",
    booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = nov,
    year = "2021",
    address = "Online and Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.emnlp-demo.1",
    doi = "10.18653/v1/2021.emnlp-demo.1",
    pages = "1--10",
    abstract = "In this paper, we present \textbf{MiSS}, an assistant for multi-style simultaneous translation. Our proposed translation system has five key features: highly accurate translation, simultaneous translation, translation for multiple text styles, back-translation for translation quality evaluation, and grammatical error correction. With this system, we aim to provide a complete translation experience for machine translation users. Our design goals are high translation accuracy, real-time translation, flexibility, and measurable translation quality. Compared with the free commercial translation systems commonly used, our translation assistance system regards the machine translation application as a more complete and fully-featured tool for users. By incorporating additional features and giving the user better control over their experience, we improve translation efficiency and performance. Additionally, our assistant system combines machine translation, grammatical error correction, and interactive edits, and uses a crowdsourcing mode to collect more data for further training to improve both the machine translation and grammatical error correction models. A short video demonstrating our system is available at \url{https://www.youtube.com/watch?v=ZGCo7KtRKd8}.",
}