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Synslator: An Interactive Machine Translation Tool with Online Learning

Authors :
Wang, Jiayi
Wang, Ke
Zhou, Fengming
Wang, Chengyu
Fu, Zhiyong
Feng, Zeyu
Zhao, Yu
Zhang, Yuqi
Publication Year :
2023

Abstract

Interactive machine translation (IMT) has emerged as a progression of the computer-aided translation paradigm, where the machine translation system and the human translator collaborate to produce high-quality translations. This paper introduces Synslator, a user-friendly computer-aided translation (CAT) tool that not only supports IMT, but is adept at online learning with real-time translation memories. To accommodate various deployment environments for CAT services, Synslator integrates two different neural translation models to handle translation memories for online learning. Additionally, the system employs a language model to enhance the fluency of translations in an interactive mode. In evaluation, we have confirmed the effectiveness of online learning through the translation models, and have observed a 13% increase in post-editing efficiency with the interactive functionalities of Synslator. A tutorial video is available at:https://youtu.be/K0vRsb2lTt8.

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2310.05025
Document Type :
Working Paper