梵语-印地语多模态机器翻译的实证分析

N. Sethi, A. Dev, Poonam Bansal
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引用次数: 0

摘要

由于在古印度宗教经文中广泛使用,梵语是最古老的土著语言之一,被理所当然地称为神的语言。然而,它在当代印度正在失去青睐。梵语在当今时代没有被广泛使用,这在很大程度上是由于缺乏翻译资源。近年来,机器翻译(MT)已经超越了常规,现在通常使用监督学习方法进行。由于缺乏可比较的梵语语料库,在无监督机器翻译领域的新研究似乎对梵语有希望。借助手动创建的梵语-印地语语言对的平行语料库,分析了构建机器翻译系统的各种建模技术,即统计和神经,以弥合梵语与其当代继任者印地语之间的差距。为了对整个领域提供一个新的观点,本工作对统计和神经机器翻译的主要优点和缺点进行了研究。我们的研究结果表明,神经机器翻译建模技术比统计机器翻译表现得更好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multimodal Machine Translation for Sanskrit-Hindi: An Empirical Analysis
Due to its extensive use in ancient Indian religious scriptures, Sanskrit is among the oldest indigenous languages and is rightfully referred to as the language of the gods. However, it is losing favour in contemporary India. Sanskrit is not widely used in current times due in large part to the lack of resources for translation into and out of it. In recent years, machine translation (MT) has improved above and beyond the norm and is now typically performed utilising supervised learning approaches. Due to the paucity of comparable corpora for Sanskrit, new research in the unsupervised MT domain appears to have promise for Sanskrit. With the aid of manually created parallel corpora for the Sanskrit-Hindi language pair, an analysis is conducted between various modelling techniques of building a machine translation system, namely Statistical and Neural, in order to bridge the gap between Sanskrit and its contemporary successor Hindi. In order to provide a fresh viewpoint on the area as a whole, the primary benefits and drawbacks of statistical and neural machine translation has been examined in this work. Our results suggest that Neural machine translation modelling technique performs better than Statistical machine translation.
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