Synslator: An Interactive Machine Translation Tool with Online Learning
Jiayi Wang Ke Wang Fengming Zhou Chengyu Wang Zhiyong Fu Zeyu Feng Yu Zhao Yuqi Zhang
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.