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

TL;DR

IntelliCAT is an interactive machine translation post-editing tool that uses neural models for quality estimation and translation suggestions, significantly improving editing efficiency and translation quality.

Contribution

The paper introduces a novel interactive translation interface with dual-level quality estimation and context-aware translation suggestions, enhancing post-editing effectiveness.

Findings

Post-editing with IntelliCAT improves translation quality.

User study shows 52.9% faster post-editing process.

The interface effectively preserves document styles.

Abstract

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/.