This paper presents a deep learning-based machine translation (MT) system that translates a sentence of subject-object-verb (SOV) structured language into subject-verb-object (SVO) structured language. This system uses recurrent neural networks (RNNs) and Encodings. Encode embedded RNNs generate a set of numbers from the input sentence, where the second RNNs generate the output from these sets of numbers. Three popular datasets of SOV structured language i.e., EMILLE corpus, Prothom-Alo corpus and Punjabi Monolingual Text Corpus ILCI-II are used as two different case-study to validate. In our experimental case-study 1, for the EMILLE corpus and Prothom-Alo corpus dataset, we have achieved 0.742, 4.11 and 0.18, respectively as Bilingual Evaluation Understudy (BLEU), NIST (metric) and tertiary entrance rank scores. Another case-study for Punjabi Monolingual Text Corpus ILCI-II dataset achieved a BLEU score of 0.75. Our results can be compared with the state-of-the-art results.

本文提出了一种基于深度学习的机器翻译（MT）系统，该系统将主语-宾语（SOV）结构语言的句子翻译为主语-宾语（SVO）结构语言。该系统使用递归神经网络（RNN）和编码。编码嵌入的RNN从输入句子生成一组数字，第二个RNN从这些数字生成输出。使用三个流行的SOV结构化语言数据集，即EMILLE语料库，Prothom-Alo语料库和Punjabi单语文本语料库ILCI-II作为两个不同的案例研究进行验证。在我们的实验案例研究1中，对于EMILLE语料库和Prothom-Alo语料库数据集，我们的双语评估学习（BLEU），NIST（度量）和三级入学等级得分分别达到0.742、4.11和0.18。旁遮普单语文本语料库ILCI-II数据集的另一个案例研究获得了0.75的BLEU评分。我们的结果可以与最新的结果进行比较。