# IntelliCAT: Intelligent Machine Translation Post-Editing with Quality Estimation and Translation Suggestion

arXiv: https://arxiv.org/abs/2105.12172 · HTML: https://mlsanity.com/paper/2105.12172/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 38 references that are in the map.
- First posted on arXiv 25 May 2021.

## Builds on (most cited first, 8 of 8)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015)
- [Cross-lingual Language Model Pretraining](https://mlsanity.com/paper/1901.07291/) (2019)
- [SpanBERT: Improving Pre-training by Representing and Predicting Spans](https://mlsanity.com/paper/1907.10529/) (2019)
- [Subword Regularization: Improving Neural Network Translation Models with Multiple Subword Candidates](https://mlsanity.com/paper/1804.10959/) (2018)
- [OpenNMT: Open-source Toolkit for Neural Machine Translation](https://mlsanity.com/paper/1709.03815/) (2017)
- [Jointly Learning to Align and Translate with Transformer Models](https://mlsanity.com/paper/1909.02074/) (2019)

## Built on by (most cited first, 11 of 11)

- [WeTS: A Benchmark for Translation Suggestion](https://mlsanity.com/paper/2110.05151/) (2021)
- [UDAAN: Machine Learning based Post-Editing tool for Document Translation](https://mlsanity.com/paper/2203.01644/) (2022)
- [Rethink about the Word-level Quality Estimation for Machine Translation from Human Judgement](https://mlsanity.com/paper/2209.05695/) (2022)
- [Bilingual Synchronization: Restoring Translational Relationships with Editing Operations](https://mlsanity.com/paper/2210.13163/) (2022)
- [Introducing Quality Estimation to Machine Translation Post-editing Workflow: An Empirical Study on Its Usefulness](https://mlsanity.com/paper/2507.16515/) (2025)
- [The JHU-Microsoft Submission for WMT21 Quality Estimation Shared Task](https://mlsanity.com/paper/2109.08724/) (2021)
- [Improved Data Augmentation for Translation Suggestion](https://mlsanity.com/paper/2210.06138/) (2022)
- [Looking under the Wrong Lamppost: On the Limitations of Automated Translation Quality Estimation](https://mlsanity.com/paper/2608.03577/) (2026)
- [Findings of the WMT 2022 Shared Task on Translation Suggestion](https://mlsanity.com/paper/2211.16717/) (2022)
- [INarIG: Iterative Non-autoregressive Instruct Generation Model For Word-Level Auto Completion](https://mlsanity.com/paper/2311.18200/) (2023)
- [An Energy-based Model for Word-level AutoCompletion in Computer-aided Translation](https://mlsanity.com/paper/2407.20083/) (2024)

## Abstract (arXiv)

We present IntelliCAT, an interactive translation interface with neural models that streamline the post-editing process on machine translation output. We leverage two quality estimation (QE) models at different granularities: sentence-level QE, to predict the quality of each machine-translated sentence, and word-level QE, to locate the parts of the machine-translated sentence that need correction. Additionally, we introduce a novel translation suggestion model conditioned on both the left and right contexts, providing alternatives for specific words or phrases for correction. Finally, with word alignments, IntelliCAT automatically preserves the original document's styles in the translated document. The experimental results show that post-editing based on the proposed QE and translation suggestions can significantly improve translation quality. Furthermore, a user study reveals that three features provided in IntelliCAT significantly accelerate the post-editing task, achieving a 52.9% speedup in translation time compared to translating from scratch. The interface is publicly available at https://intellicat.beringlab.com/.