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首页> 外文会议>2016 International Conference on Informatics and Computing >Improving the performance of translation process in a statistical machine translator using sequence IRSTLM translation parameters and pruning
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Improving the performance of translation process in a statistical machine translator using sequence IRSTLM translation parameters and pruning

机译：使用序列IRSTLM翻译参数和修剪来改善统计机器翻译器中翻译过程的性能

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摘要

A translation process, as a critical part of a machine translation, can be put simply as a process of decoding the meaning of the source text and re-encoding the meaning into the target language. Unfortunately, most of the translation process requires complex operations and in-depth knowledge of the languages in order to give a good quality translation. This study proposes a better approach, which does not require in-depth knowledge of the linguistic properties of the languages, but it produces a good quality translation. This statistical machine translation uses parallel corpus and statistical machine learning for English and Bahasa Indonesia. This study also evaluated 28 different parameters in IRSTLM language modeling and proposes a sequence evaluation mechanism based on a maximum evaluation of each parameter in producing a good quality translation based on NIST and BLEU. The pruning process, user interface, and the personalization of translation have a very important role in implementing of this machine translation. The result is quite promising. It shows that pruning process increases of the translation process time. The particular sequence value parameter in translation process, it also has a better performance than the other method using in-depth linguistic knowledge approaches. All these processes, including the process of parsing from a stand-alone mode to an online mode, are also discussed in detail.
机译：作为机器翻译的关键部分，翻译过程可以简单地看作是对源文本的含义进行解码并将其含义重新编码为目标语言的过程。不幸的是，大多数翻译过程需要复杂的操作和对语言的深入了解，才能提供高质量的翻译。这项研究提出了一种更好的方法，该方法不需要深入了解语言的语言特性，但可以产生高质量的翻译。这种统计机器翻译使用英语和印尼语的并行语料库和统计机器学习。这项研究还评估了IRSTLM语言建模中的28个不同参数，并提出了一种序列评估机制，该机制基于对每个参数的最大评估，从而基于NIST和BLEU生成高质量的翻译。修剪过程，用户界面和翻译的个性化在实施此机器翻译中起着非常重要的作用。结果是很有希望的。它表明修剪过程增加了翻译过程的时间。在翻译过程中，特定的序列值参数也比使用深度语言知识方法的其他方法具有更好的性能。还详细讨论了所有这些过程，包括从独立模式解析为联机模式的过程。

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