# Synslator: An Interactive Machine Translation Tool with Online Learning

arXiv: https://arxiv.org/abs/2310.05025 · HTML: https://mlsanity.com/paper/2310.05025/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 29 references that are in the map.
- First posted on arXiv 8 Oct 2023.

## Builds on (most cited first, 11 of 11)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Nearest Neighbor Machine Translation](https://mlsanity.com/paper/2010.00710/) (2020)
- [Non-Parametric Adaptation for Neural Machine Translation](https://mlsanity.com/paper/1903.00058/) (2019)
- [Fast Domain Adaptation for Neural Machine Translation](https://mlsanity.com/paper/1612.06897/) (2016)
- [Simple and Scalable Nearest Neighbor Machine Translation](https://mlsanity.com/paper/2302.12188/) (2023)
- [Automatic Post-Editing for Machine Translation](https://mlsanity.com/paper/1910.08592/) (2019)
- [Computer Assisted Translation with Neural Quality Estimation and Automatic Post-Editing](https://mlsanity.com/paper/2009.09126/) (2020)
- [Easy Guided Decoding in Providing Suggestions for Interactive Machine Translation](https://mlsanity.com/paper/2211.07093/) (2022)
- [TSMind: Alibaba and Soochow University's Submission to the WMT22 Translation Suggestion Task](https://mlsanity.com/paper/2211.08987/) (2022)

## Built on by (most cited first, 1 of 1)

- [LINC: Supporting Language Independent Communication and Comprehension to Enhance Contribution in Multilingual Collaborative Meetings](https://mlsanity.com/paper/2504.18988/) (2025)

## 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.