Abstract

In this paper, we present 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 https://www.youtube.com/watch?v=ZGCo7KtRKd8.

Highlights

  • Pared with the free commercial translation systems commonly used, our translation assistance system regards the machine translation application as a more complete and fullyfeatured tool for users

  • For the Neural Machine Translation (NMT) component, we chose the WMT2020 test set newstest2020 as the evaluation set for formal EN-ZH and EN-JA translation and the development set of the AI Challenger 2018 competition as the evaluation set for oral ZH-EN translation

  • We evaluated English at the word level and Chinese and Japanese at the character level

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Summary

Introduction

Pared with the free commercial translation systems commonly used, our translation assistance system regards the machine translation application as a more complete and fullyfeatured tool for users. By incorporating additional features and giving the user better control over their experience, we improve translation efficiency and performance. Neural machine translation has made tremendous improvements and is relatively highperforming, because human language is so complex, machine translation is often still only used as an assistance tool rather than the sole entity responsible for translation. As NMT is still very imprecise, these web services fall short, as they do not provide sufficient information to users in how good each translation is, which is pertinent to those who have not mastered the target language. Simultaual translation works well, with the increasing neous machine translation, translating sentences in frequency of international communication, tradi- real-time while the user speaks or types, can signiftional manual translation far from meets demand, icantly reduce this translation time, but its perfor-

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