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A pool-based active learning method for improving Farsi-English Machine Translation system

机译：基于池的主动学习方法改进波斯英语机器翻译系统

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摘要

In this paper we try to alleviate the problem of scares resources for developing Farsi-English Statistical Machine Translation system (SMT). It is done by applying Active Learning (AL) idea to choose more informative sentences to be translated by a human and then be added to the base-line corpus. While using the human translations is worthless in compare to the other approaches of corpus gathering (like automatic approaches), it is more costly too. So, in this way we can improve the translation system with less cost. This is done in intricate to human translator. Applying Active learning idea to a SMT system, changes it to a system which can improve its based-line corpus by asking for the essential data which directly leads to the system improvement. On the other hand, combination of AL idea with SMT is a way of using source side monolingual resources for improving SMT systems which is ignored in the original theory of SMT. Our results for Farsi-English system shows improvement in compare to random sentence selection.
机译：在本文中，我们试图缓解开发波斯英语统计机器翻译系统（SMT）的恐慌资源的问题。通过应用主动学习（AL）想法来选择更多内容丰富的句子以供人类翻译，然后将其添加到基准语料库中来完成。尽管与人工语料收集的其他方法（如自动方法）相比，使用人工翻译毫无价值，但成本也更高。因此，通过这种方式，我们可以以更低的成本改进翻译系统。这对于人工翻译来说是很复杂的。将主动学习的思想应用于SMT系统，将其更改为可以通过请求直接导致系统改进的基本数据来改进其基础语料库的系统。另一方面，AL思想与SMT的结合是一种利用源端单语资源来改善SMT系统的方法，这在SMT的原始理论中被忽略了。我们针对波斯英语系统的结果显示，与随机句子选择相比，该方法有所改进。

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