Explicitation in Neural Machine Translation
Explicitation in Neural Machine Translation
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神经机器翻译的显化
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发表时间：
2020
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通讯作者：
Ralph Krüger
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文献类型：
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作者：
Ralph Krüger
本文关注的是以下问题：在何种程度上神经机器翻译（NMT）-一个相对较新的方法，机器翻译（MT），它可以利用更丰富的上下文信息比以前的MT架构-执行显式转换在翻译和如何实现这些转变的语言条件？为了回答这个问题，本文试图找出机器翻译版本的二氧化碳捕获和储存的研究报告中的显化的实例。机器翻译的文本是使用公开可用的通用NMT系统DeepL创建的。在之前的一个研究项目中，对研究报告的人工翻译进行了分析，以确定显式和隐式的实例（Krüger 2015）。在对DeepL输出中识别出的显化移位的频率和分布与研究报告的人工翻译中识别出的显化移位进行了简要的定量分析之后，本文详细分析了DeepL执行各种显化移位的几个例子。定量和定性分析的目的是产生一个国家的最先进的神经机器翻译系统的能力，以执行显化转换的翻译尝试性的图片。由于显化在本文中被理解为翻译文本-语境交互的指示器，NMT的显化性能可以-在某种程度上-被认为是这种新的MT架构的“语境意识”的指示。
This paper is concerned with the following question: to what extent does neural machine translation (NMT) – a relatively new approach to machine translation (MT), which can draw on richer contextual information than previous MT architectures – perform explicitation shifts in translation and how are these shifts realised in linguistic terms? In order to answer this question, the paper attempts to identify instances of explicitation in the machine-translated version of a research report on carbon dioxide capture and storage. The machine-translated text was created using the publicly available generic NMT system DeepL. The human translation of the research report was analysed in a prior research project for instances of explicitation and implicitation (Krüger 2015). After a brief quantitative di scussion of the frequency and distribution of explicitation shifts identified in the DeepL output as compared to the shifts identified in the human translation of the research report, the paper analyses in detail several examples in which DeepL performed explicitation shifts of various kinds. The quantitative and qualitative analyses are intended to yield a tentative picture of the capacity of state-of-the art neural machine translation systems to perform explicitation shifts in translation. As explicitation is understood in this article as an indicator of translational text–context interaction, the explicitation performance of NMT can – to some extent – be taken to be indicative of the “contextual awareness” of this new MT architecture.