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Neural Machine Translation for Cebuano to Tagalog with Subword Unit Translation

机译:带有子字单元翻译的eBuano到Tagalog的神经机翻译

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The Philippines is an archipelago composed of 7, 641 different islands with more than 150 different languages. This linguistic differences and diversity, though may be seen as a beautiful feature, have contributed to the difficulty in the promotion of educational and cultural development of different domains in the country. An effective machine translation system solely dedicated to cater Philippine languages will surely help bridge this gap. In this research work, a never before applied approach for language translation to a Philippine language was used for a Cebuano to Tagalog translator. A Recurrent Neural Network was used to implement the translator using OpenNMT sequence modeling tool in TensorFlow. The performance of the translation was evaluated using the BLEU Score metric. For the Cebuano to Tagalog translation, BLEU produced a score of 20.01. A subword unit translation for verbs and copyable approach was performed where commonly seen mistranslated words from the source to the target were corrected. The BLEU score increased to 22.87. Though slightly higher, this score still indicates that the translation is somehow understandable but is not yet considered as a good translation.
机译:菲律宾是一个由7,641个不同岛屿组成的群岛,不同的岛屿超过150种不同的语言。这种语言差异和多样性,但可能被视为一个美丽的特征,为促进了该国不同域的教育和文化发展的困难。一款仅致力于迎合菲律宾语言的有效机器翻译系统肯定会帮助弥合这一差距。在这项研究工作中,从未在应用于菲律宾语言的语言翻译方法之前,用于泰阪译者的培训语言。经常性神经网络用于使用Tensorflow中的OpenNMT序列建模工具实现翻译器。使用BLEU评分度量评估翻译的性能。对于泰诺戈转换的CeBuano来说,Bleu产生了20.01分。执行用于动词和可复制方法的子字单元翻译,其中纠正了来自源到目标的源自误差的单词。 BLEU分数增加到22.87。虽然稍高,但这个分数仍然表明翻译是一种以某种方式可以理解但尚未被视为一个不错的翻译。

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