Babel - Volume 69, Issue 4, 2023

This article discusses the challenges that the rendering of the pragmatic implications of texts into a target language posed for human translators and, by extension, for automated translation. It starts by discussing the importance of pragmatics, focusing on two concepts that have received much attention on the part of pragmaticians as well as translation scholars, namely implicatures and politeness. It moves to on to present some of the most notable publications on the interface between pragmatics and machine translation. These illustrate that the interest in the pragmatic value of language has not succeeded in advancing the integration of pragmatics into automated translation. Drawing on Kesckes and House, the last section discusses two concepts to be considered regarding the role of pragmatics in intercultural mediation.

Growing interest has been noted in applying AI-powered machine translation (MT) to literary translation, hailed as the last bastion of human translation. Despite achieving considerable progress in this field, research has either ignored or underestimated the particularity, complexity, and cultural significance of literary translation, which can be examined from a sociological approach. Drawing on the sociological theories of Bourdieu, Latour, Callon, and Baudrillard, the present paper analyses the innate nature of literary translation and highlights three fundamental issues that need to be addressed in applying MT to literary texts. First, the poetics of literary translation is built on human translators’ long-acquired habitus, thus, in the case of MT, an algorithm comparable to the creative human habitus must be derived if MT aspires to take on the role of the human translator. Second, literary translation constitutes a dynamic network connected by various human and non-human actors, thus the aspects not included in the interlingual transference of MT should be compensated through more effective interactions between the machine and other actors. Third, the cultural-ethical issues related to MT should be thoroughly examined because the present MT of literary texts is a machine simulation of the psychological human translation, which undermines both the meaning generation of literary translation and the knowledge accumulation of cultural production. Therefore, literary translation must be handled by qualified human translators until we can undoubtedly ensure that MT can be effectively and safely applied to literary texts.

By comparing the Chinese, English and French versions of “Exhortations of Learning” and “On Building a Human Community with a Shared Future,” translated by human translators and the neural machine translation systems respectively, this essay finds out that human translators have addressed the political and ideological factors more tactfully while the working mechanism of the neural machine translation system lacks the formers’ judgment, consideration, flexibility and subjectivity. Moreover, unlike human translators, the neural machine system is not capable of activities such as summarizing the source texts, making comments or annotating. But on the other hand, the neural machine translation system has the advantages of its own. Not affected by bias like human translators, it could perform the translation faster and with a rather objective stance. All in all, there is still a long way to go before it can reveal the political and ideological factors in ways as human translators can achieve.

The article invokes John Henry’s fatal competition with the rock-driving machine, a legendary exemplar of resistance to automation, as a speculative analogue of Walter Benjamin’s 1923 essay “Die Aufgabe des Übersetzers,” read as a metaphysical attempt to develop a workable alternative to the kind of mechanizable translating he hated. Benjamin’s practical work with Baudelaire and Proust and the others is read tentatively as the forerunners to machine translation eighty-plus years later – working with the meanings of individual words in sentential chunks, striving to organize them along the lines of statistical usage in the target language, and incrementally learning with each translation job to assimilate actual reproductions of sentential meaning more and more accurately to statistical usage. “Die Aufgabe des Übersetzers” by contrast, read as his anti-MT manifesto, opens up to a somatic phenomenology of felt (super)human life.

Technological advancement has brought changes to many professions across the world. Furthermore, it has triggered a discussion about ethical issues. Machine Translation (MT) or Computer-Aided Translation (CAT) has vastly increased work efficiency and technology-related ethical issues are gaining academic attention these days. However, the discussion of ethical issues for literary translation against the backdrop of Artificial Intelligence (AI) is limited. After a quick review of the stages of MT, this paper will delve into literary translation’s emerging ethical issues, for example, the literary translator’s professional identity and copyright. Copyright ethics is an indispensable part of AI-enabled literary translation since training data and participatory NMT involve copyright issues. This study revealed that technological advancement will facilitate literary translation. However, no direct evidence exists that machine translation will replace human translators. Given the new working mode of “multi-players” or participatory translation, ethical issues arising from the human and machine interaction merit further academic inquiry.

This study examines the performance of the neural machine translation system DeepL in translating Shakespeare’s plays Coriolanus and The Merchant of Venice. The aim here is to explore the strengths and limitations of an AI-based English-Chinese translation of literary texts. Adopting a corpus-based approach, the study investigates the accuracy and fluency rates, the linguistic features, and the use of various methods of translation in the Chinese translations of Shakespeare’s plays conducted via DeepL. It compares these to the translations by Liang Shiqiu, a well-known Chinese translator. The study finds that DeepL performs well in translating these works, with an accuracy and fluency rate of above 80% in sampled texts, showing the potential of the use of neural machine translation in translating literary texts across distant languages. Our research further reveals that the DeepL translations exhibit a certain degree of creativity in their use of translation methods such as addition, explicitation, conversion and shift of perspective, and in the use of Chinese sentence-final modal particles, as well as Chinese modal verbs. On the other hand, the system appears to be limited in that a certain amount of translation errors are present, including literal translations.

This paper revisits Roman Jakobson’s literaturnost within the framework of modern poetics and in light of the challenge posed by artificial intelligence to human literary translation. It is argued that literary translation is, in essence, more of a project of code transposition and meaning generation than of message transmission. Furthermore, it is noted that although algorithms can process certain literary devices, they are currently unable to process the polysystemic relations that constitute an artwork’s literaturnost. Consequently, it is emphasized that meaning transferal, meaning generation, and the revolt against meaning will continue to pose barriers for future machine translation even in the long term.