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

机译:Intellicat:智能机翻译后编辑质量估算和翻译建议

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摘要

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 lor 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.
机译:我们呈现IntelliCat,一个与神经模型的交互式翻译界面,用于简化机器翻译输出后的编辑后进程。我们利用不同粒度的两个质量估计(QE)模型:句子级QE,以预测每个机器翻译句子和字级QE的质量,以找到需要校正的机器翻译句子的部分。此外,我们介绍了一个新颖的翻译建议模型在左下方和右侧上下文中,提供替代品的特定词或短语进行校正。最后,通过字对齐,Intellicat自动保留在翻译的文档中的原始文档的样式。实验结果表明,基于提议的QE和翻译建议的后编辑可以显着提高翻译质量。此外,用户学习揭示了Intellicat中提供的三个功能显着加速了编辑后的任务,与从头转换相比,在翻译时间内实现了52.9%的加速。

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