Source-target mapping model of streaming data flow for machine translation

Jolanta Mizera-Pietraszko, G. Kolaczek, Ricardo Rodriguez Jorge
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Abstract

Streaming information flow allows identification of linguistic similarities between language pairs in real time as it relies on pattern recognition of grammar rules, semantics and pronunciation especially when analyzing so called international terms, syntax of the language family as well as tenses transitivity between the languages. Overall, it provides a backbone translation knowledge for building automatic translation system that facilitates processing any of various abstract entities which combine to specify underlying phonological, morphological, semantic and syntactic properties of linguistic forms and that act as the targets of linguistic rules and operations in a source language following professional human translator. Streaming data flow is a process of mining source data into target language transformation during which any inference impedes the system effectiveness by producing incorrect translation. We address a research problem of exploring streaming data from source-target parallels for detection of linguistic similarities between languages originated from different groups.
机器翻译流数据流的源-目标映射模型
流信息流可以实时识别语言对之间的语言相似性,因为它依赖于语法规则、语义和发音的模式识别,特别是在分析所谓的国际术语、语族语法以及语言之间的时态及物性时。总的来说,它为构建自动翻译系统提供了一个主干翻译知识,该系统可以方便地处理任何各种抽象实体,这些实体组合在一起指定语言形式的语音,形态,语义和句法属性,并作为专业翻译人员在源语言中作为语言规则和操作的目标。流数据流是将源数据挖掘到目标语言转换的过程,在此过程中,任何推理都会产生错误的翻译,从而影响系统的有效性。我们解决了一个研究问题,即从源-目标平行关系中探索流数据,以检测来自不同群体的语言之间的语言相似性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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