從低資源數據中構建一個好的機器翻譯器是最近的挑戰之一。為了解決這個問題，出現了轉移學習並為此問題提供了解決方案。機器翻譯器的另一個重要問題是構建沒有平行語料庫資源的良好機器翻譯器。該問題的解決方案之一是使用師生框架來生成生成並行語料庫的源-目標機器。在本文中，我們試圖利用機器生成的語料庫來解決構建低資源機器翻譯的問題。基本上，我們結合解決低資源和零資源機器翻譯問題的解決方案來解決資源機器翻譯本身的問題。我們的想法是生成大量機器生成的數據，我們可以將這些數據用於培訓，然後使用轉移學習繼續使用低資源數據進行培訓。我們在本論文中的貢獻證明，機器生成的數據可以幫助我們培訓低資源的人為數據。據我們所知，由於數據稀缺問題，之前沒有討論中印機器翻譯的論文。因此，我們以英語為中心進行中印機器翻譯，並利用一些馬來西亞語料庫。;Build a good machine translator out of low resource data is one of the challenges recently. In order to tackle this issue, transfer learning came up and bring a solution to this problem. Another substantial problem for machine translator is to build the good machine translator with no resource of parallel corpus available. One of the solutions for this problem is to use the teacher-student framework to produce a source-target machine-generated parallel corpus.In this thesis, we tried to tackle the problem of building low resource machine translatorby utilizing machine-generated corpus. Basically, we combinethe solution for tackling low resource and zero resource machine translation problem to address the problem of low resource machine translation itself. The idea is to produce huge machine-generated data that we can use for training and then use transfer learning to continue the training with low resource data.Our contribution in this thesis is proved that machine-generated data can help us to train low resource human-generated data. As far as we know, there is no previous paper that discusses Chinese-Indonesian machine translation because of the data scarcity problem. Therefore, we do Chinese-Indonesian machine translation with English as a pivot and utilize some of the Malaysian corpora.
