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.

中文翻译：

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神经机器翻译中的显式

本文关注以下问题：神经机器翻译 (NMT) - 一种相对较新的机器翻译 (MT) 方法，可以利用比以前的 MT 架构更丰富的上下文信息 - 在翻译中执行显式转换的程度如何？这些转变是否以语言的形式实现？为了回答这个问题，本文试图在关于二氧化碳捕获和储存的研究报告的机器翻译版本中找出明确的实例。机器翻译文本是使用公开可用的通用 NMT 系统 DeepL 创建的。研究报告的人工翻译在先前的研究项目中分析了显化和隐含的实例（Krüger 2015）。在对 DeepL 输出中识别出的显化变化的频率和分布与研究报告的人工翻译中识别出的变化进行了简短的定量讨论之后，本文详细分析了 DeepL 执行各种显化变化的几个示例. 定量和定性分析旨在初步了解最先进的神经机器翻译系统在翻译中执行显式转换的能力。由于本文将显化理解为翻译文本-上下文交互的指标，NMT 的显化性能在某种程度上可以被认为是这种新 MT 架构的“上下文感知”的指示。