论文标题
神经机器翻译系统的指示语言 - 一种基于注意力的方法
Neural Machine Translation System of Indic Languages -- An Attention based Approach
论文作者
论文摘要
神经机器翻译（NMT）是一种最近有效的技术，可以在传统机器翻译技术的比较中取得显着改进。为Gujarati语言开发的拟议的神经机器翻译模型包含带有注意机制的编码器。在印度，几乎所有语言都源自其祖先语言-Sanskrit。他们具有不可避免的相似性，包括词汇和命名实体相似性。转化为指示语言始终是一项具有挑战性的任务。在本文中，我们介绍了神经机器翻译系统（NMT），该系统可以有效地翻译诸如印地语和古吉拉特语之类的语言，这些语言共同涵盖了该国总扬声器的58.49％以上。我们已经将NMT模型的性能与自动评估矩阵（例如BLEU，困惑和TER矩阵）进行了比较。还介绍了我们的网络与Google翻译的比较，在英语gujarati翻译上，它的表现优于6个BLEU得分。
Neural machine translation (NMT) is a recent and effective technique which led to remarkable improvements in comparison of conventional machine translation techniques. Proposed neural machine translation model developed for the Gujarati language contains encoder-decoder with attention mechanism. In India, almost all the languages are originated from their ancestral language - Sanskrit. They are having inevitable similarities including lexical and named entity similarity. Translating into Indic languages is always be a challenging task. In this paper, we have presented the neural machine translation system (NMT) that can efficiently translate Indic languages like Hindi and Gujarati that together covers more than 58.49 percentage of total speakers in the country. We have compared the performance of our NMT model with automatic evaluation matrices such as BLEU, perplexity and TER matrix. The comparison of our network with Google translate is also presented where it outperformed with a margin of 6 BLEU score on English-Gujarati translation.
