# TranSmart: A Practical Interactive Machine Translation System

arXiv: https://arxiv.org/abs/2105.13072 · HTML: https://mlsanity.com/paper/2105.13072/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 36 of its 91 references that are in the map.
- First posted on arXiv 27 May 2021.

## Builds on (most cited first, 30 of 36)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (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)
- [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015)
- [fairseq: A Fast, Extensible Toolkit for Sequence Modeling](https://mlsanity.com/paper/1904.01038/) (2019)
- [Convolutional Sequence to Sequence Learning](https://mlsanity.com/paper/1705.03122/) (2017)
- [OpenNMT: Open-source Toolkit for Neural Machine Translation](https://mlsanity.com/paper/1709.03815/) (2017)
- [Lexically Constrained Decoding for Sequence Generation Using Grid Beam Search](https://mlsanity.com/paper/1704.07138/) (2017)
- [Pay Less Attention with Lightweight and Dynamic Convolutions](https://mlsanity.com/paper/1901.10430/) (2019)
- [Nearest Neighbor Machine Translation](https://mlsanity.com/paper/2010.00710/) (2020)
- [Scaling Neural Machine Translation](https://mlsanity.com/paper/1806.00187/) (2018)
- [Fast Lexically Constrained Decoding with Dynamic Beam Allocation for Neural Machine Translation](https://mlsanity.com/paper/1804.06609/) (2018)
- [Tensor2Tensor for Neural Machine Translation](https://mlsanity.com/paper/1803.07416/) (2018)
- [Training Neural Machine Translation To Apply Terminology Constraints](https://mlsanity.com/paper/1906.01105/) (2019)
- [A Survey of Domain Adaptation for Neural Machine Translation](https://mlsanity.com/paper/1806.00258/) (2018)
- [Guiding Neural Machine Translation with Retrieved Translation Pieces](https://mlsanity.com/paper/1804.02559/) (2018)
- [Neural Machine Translation with Monolingual Translation Memory](https://mlsanity.com/paper/2105.11269/) (2021)
- [Code-Switching for Enhancing NMT with Pre-Specified Translation](https://mlsanity.com/paper/1904.09107/) (2019)
- [Exploiting Cross-Sentence Context for Neural Machine Translation](https://mlsanity.com/paper/1704.04347/) (2017)
- [compare-mt: A Tool for Holistic Comparison of Language Generation Systems](https://mlsanity.com/paper/1903.07926/) (2019)
- [Accurate Word Alignment Induction from Neural Machine Translation](https://mlsanity.com/paper/2004.14837/) (2020)
- [Neural Machine Translation with Supervised Attention](https://mlsanity.com/paper/1609.04186/) (2016)
- [Saliency-driven Word Alignment Interpretation for Neural Machine Translation](https://mlsanity.com/paper/1906.10282/) (2019)
- [Mask-Align: Self-Supervised Neural Word Alignment](https://mlsanity.com/paper/2012.07162/) (2020)
- [Data Rejuvenation: Exploiting Inactive Training Examples for Neural Machine Translation](https://mlsanity.com/paper/2010.02552/) (2020)
- [Translating Pro-Drop Languages with Reconstruction Models](https://mlsanity.com/paper/1801.03257/) (2018)
- [Evaluating Explanation Methods for Neural Machine Translation](https://mlsanity.com/paper/2005.01672/) (2020)
- [Automatic Construction of Discourse Corpora for Dialogue Translation](https://mlsanity.com/paper/1605.06770/) (2016)

## Built on by (most cited first, 17 of 17)

- [Document-Level Machine Translation with Large Language Models](https://mlsanity.com/paper/2304.02210/) (2023)
- [With Greater Text Comes Greater Necessity: Inference-Time Training Helps Long Text Generation](https://mlsanity.com/paper/2401.11504/) (2024)
- [One Model, Multiple Modalities: A Sparsely Activated Approach for Text, Sound, Image, Video and Code](https://mlsanity.com/paper/2205.06126/) (2022)
- [Findings of the WMT 2023 Shared Task on Discourse-Level Literary Translation: A Fresh Orb in the Cosmos of LLMs](https://mlsanity.com/paper/2311.03127/) (2023)
- [Self-Training Sampling with Monolingual Data Uncertainty for Neural Machine Translation](https://mlsanity.com/paper/2106.00941/) (2021)
- [On the Complementarity between Pre-Training and Back-Translation for Neural Machine Translation](https://mlsanity.com/paper/2110.01811/) (2021)
- [On the Cultural Gap in Text-to-Image Generation](https://mlsanity.com/paper/2307.02971/) (2023)
- [A Survey on Zero Pronoun Translation](https://mlsanity.com/paper/2305.10196/) (2023)
- [GWLAN: General Word-Level AutocompletioN for Computer-Aided Translation](https://mlsanity.com/paper/2105.14913/) (2021)
- [Interactive Machine Learning: A State of the Art Review](https://mlsanity.com/paper/2207.06196/) (2022)
- [Skill Disentanglement for Imitation Learning from Suboptimal Demonstrations](https://mlsanity.com/paper/2306.07919/) (2023)
- [Bilingual Synchronization: Restoring Translational Relationships with Editing Operations](https://mlsanity.com/paper/2210.13163/) (2022)
- [Rethinking Translation Memory Augmented Neural Machine Translation](https://mlsanity.com/paper/2306.06948/) (2023)
- [Towards General Error Diagnosis via Behavioral Testing in Machine Translation](https://mlsanity.com/paper/2310.13362/) (2023)
- [Rethinking Word-Level Auto-Completion in Computer-Aided Translation](https://mlsanity.com/paper/2310.14523/) (2023)
- [Improving Neural Machine Translation by Multi-Knowledge Integration with Prompting](https://mlsanity.com/paper/2312.04807/) (2023)
- [An Energy-based Model for Word-level AutoCompletion in Computer-aided Translation](https://mlsanity.com/paper/2407.20083/) (2024)

## Abstract (arXiv)

Automatic machine translation is super efficient to produce translations yet their quality is not guaranteed. This technique report introduces TranSmart, a practical human-machine interactive translation system that is able to trade off translation quality and efficiency. Compared to existing publicly available interactive translation systems, TranSmart supports three key features, word-level autocompletion, sentence-level autocompletion and translation memory. By word-level and sentence-level autocompletion, TranSmart allows users to interactively translate words in their own manners rather than the strict manner from left to right. In addition, TranSmart has the potential to avoid similar translation mistakes by using translated sentences in history as its memory. This report presents major functions of TranSmart, algorithms for achieving these functions, how to use the TranSmart APIs, and evaluation results of some key functions. TranSmart is publicly available at its homepage (https://transmart.qq.com).