mlsanityData to 7 Oct 2026

TranSmart: A Practical Interactive Machine Translation System

arXiv 2105.13072 ↗First posted 27 May 2021cs.CLcited by 17

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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).

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). Builds on 36 of its 91 references that are in the map. First posted on arXiv 27 May 2021.

Builds on

36 of 91 references.

References · 36

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24 more
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Citing papers per year: 2021: 3; 2022: 3; 2023: 9; 2024: 2.

17 papers.

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  1. 2023Document-Level Machine Translation with Large Language Modelscited by 90
  2. 2024With Greater Text Comes Greater Necessity: Inference-Time Training Helps Long Text Generationcited by 36
  3. 2022One Model, Multiple Modalities: A Sparsely Activated Approach for Text, Sound, Image, Video and Codecited by 26
  4. 2023Findings of the WMT 2023 Shared Task on Discourse-Level Literary Translation: A Fresh Orb in the Cosmos of LLMscited by 25
  5. 2021Self-Training Sampling with Monolingual Data Uncertainty for Neural Machine Translationcited by 17
  6. 2021On the Complementarity between Pre-Training and Back-Translation for Neural Machine Translationcited by 14
  7. 2023On the Cultural Gap in Text-to-Image Generationcited by 10
  8. 2023A Survey on Zero Pronoun Translationcited by 9
  9. 2021GWLAN: General Word-Level AutocompletioN for Computer-Aided Translationcited by 7
  10. 2022Interactive Machine Learning: A State of the Art Reviewcited by 6
  11. 2023Skill Disentanglement for Imitation Learning from Suboptimal Demonstrationscited by 5
  12. 2022Bilingual Synchronization: Restoring Translational Relationships with Editing Operationscited by 2
5 more
  1. 2023Rethinking Translation Memory Augmented Neural Machine Translationcited by 1
  2. 2023Towards General Error Diagnosis via Behavioral Testing in Machine Translationcited by 1
  3. 2023Rethinking Word-Level Auto-Completion in Computer-Aided Translationcited by 0
  4. 2023Improving Neural Machine Translation by Multi-Knowledge Integration with Promptingcited by 0
  5. 2024An Energy-based Model for Word-level AutoCompletion in Computer-aided Translationcited by 0