Synslator: An Interactive Machine Translation Tool with Online Learning
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 11 of its 29 references that are in the map. First posted on arXiv 8 Oct 2023.
Builds on
11 of 29 references.
References · 11
- 2017Attention Is All You Needcited by 59,932
- 2014Neural Machine Translation by Jointly Learning to Align and Translatecited by 4,530
- 2014Sequence to Sequence Learning with Neural Networkscited by 2,974
- 2020Nearest Neighbor Machine Translationcited by 209
- 2019Non-Parametric Adaptation for Neural Machine Translationcited by 34
- 2016Fast Domain Adaptation for Neural Machine Translationcited by 28
- 2023Simple and Scalable Nearest Neighbor Machine Translationcited by 12
- 2019Automatic Post-Editing for Machine Translationcited by 8
- 2020Computer Assisted Translation with Neural Quality Estimation and Automatic Post-Editingcited by 4
- 2022Easy Guided Decoding in Providing Suggestions for Interactive Machine Translationcited by 2
- 2022TSMind: Alibaba and Soochow University's Submission to the WMT22 Translation Suggestion Taskcited by 1
Built on by
Citing papers per year: 2025: 1.
1 paper.