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Forest Rescoring: Faster Decoding with Integrated Language Models

机译:森林抢救:用综合语言模型更快地解码

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Efficient decoding has been a fundamental problem in machine translation, especially with an integrated language model which is essential for achieving good translation quality. We develop faster approaches for this problem based on k-best parsing algorithms and demonstrate their effectiveness on both phrase-based and syntax-based MT systems. In both cases, our methods achieve significant speed improvements, often by more than a factor of ten, over the conventional beam-search method at the same levels of search error and translation accuracy.
机译:有效的解码是机器翻译中的一个基本问题,特别是综合语言模型,这对于实现良好的翻译质量至关重要。基于K-Best解析算法,我们在基于K-Best Sysing算法上展示了更快的方法,并在基于短语和基于语法的MT系统上展示了它们的有效性。在这两种情况下,我们的方法在相同级别的搜索错误和转换精度的传统光束搜索方法上实现了显着的速度改善,通常超过十倍。

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