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
this https URL

本文介绍了一种基于智能模型的交互式翻译界面 IntelliCAT，该界面利用两个不同粒度的质量估计模型来实现机器翻译的后编辑过程，进一步提出了一种新的翻译建议模型，能够根据上下文条件性地提供特定单词或短语的替代方案，最终可以自动地保留原始文档的样式。实验结果表明，基于所提出的质量估计和翻译建议的后编辑可以显著提高翻译质量。此外，用户研究表明，IntelliCAT 提供的三个特性显著加速了后编辑任务，相对于从头开始翻译，速度提升了 52.9％。