Articles

“Translation Studies Today: Translation and Competition of Intelligences.” The 6th International Conference took place on the 19-20th of March 2021.

The Innovation in Interpreting Summit took place from February 23, 2021 to February 25, 2021.

The article will analyze the possibilities of developing special scenarios for creating a learning environment through the use of digital platforms, as well as the degree to which the best display of pedagogical experience and following leading didactic principles are combined in the course of online Russian language training for future Chinese translators. In addition, the author draws the reader’s attention to the importance of inter­subject relations and identifies a number of unresolved issues related to the practice of developing skills and abilities in the field of foreign language communication in digital format.

The rapid development of artificial intelligence has created great opportunities and challenges for translation activities and set new tasks for the training of future translators. The elements covered by traditional translation competences need to be supplemented and improved in the era of artificial intelligence. Based on the Sino­Russian translation demand and translation practice, the article analyzes the changes that technology has brought to Sino­Russian interpretation. It is noted that the discipline “Translation from Chinese into Russian” in Chinese universities is mainly intended, first of all, for the era of human translation and is not adapted to the era of artificial intelligence. By combining various requirements imposed on translators in the field of artificial intelligence, we have developed a course aimed at developing important translation competences of future translators as native Chinese speakers, including the competence of post­editing from the native language into Russian. The article comprehensively describes and analyzes the model of the formation of this competence in the era of artificial intelligence.

The history of machine translation can be seen as a challenge to human thought and intellect from its inception up to the present day. Currently, four main types of machine translation are recognized: first, Rule­Based Machine Translation (RBMT); second, Statistical Machine Translation (SMT); third, Hybrid Machine Translation (HMT), which arose at the intersection of the two aforementioned types; fourth, Neural Machine Translation (NMT).

The history of machine translation has developed rapidly, with ups and downs, periods of inspiration and disappointment. However, according to M.N. Marchuk, the topic of machine translation will always be relevant for a number of reasons, one of which is people’s desire to know the boundaries of the impossible.

The technologies we use to translate information between languages continue to evolve. For example, Google Translator, Toolkit, Skype translate content into over 130 languages. Multilingual Information Search (CLIR) software (such as Google Search) allows users to search information in one language and get results in multiple languages at once. Social media platforms like Facebook provide instant translation of messages, so content that was once limited to a specific audience is now more accessible. While these digital translation tools certainly provide more options and resources for people who want to interact in different languages on the Internet, users can still face challenges. Digital translation software is developed using machine algorithms that primarily measure grammatical and lexical accuracy rather than relying on user experience research. Most modern digital translation tools operate on a translation­as­replacement model. Users enter a word or phrase in one language, select the desired target language, and click Translate. An equivalent word (or a set of equivalent words) in the target language appears on the screen.

The reliance on machine algorithms and the lack of user experience research conducted through digital translation have left large gaps in the capabilities of this software. To improve digital translation tools, we need to understand how users navigate the language on the Internet during their daily activities, as they incorporate cultural knowledge and localized experience into their translations.

The paper deals with the development of bilingual professional terminology in the field of construction, which is important in the era of the development of artificial intelligence and machine translation. What challenges are facing terminologists today?

Despite the momentous advance of digital technologies, the concerns regarding the demise of a translator’s job are a bit premature. There might come a time in some foreseeable future when the digital translation will be indistinguishable from the translation performed by a human but currently, however, this is definitely not the case — too radical is the divide between the two.

Unquestionably, the easier the task at hand, i.e., the more literal and unambiguous the lexical items constituting the original text are, the higher the quality of the digital translation. Will the AI’s scope of references be sufficient for adequate choices to be made within the language pair English — Russian, wherein polysemy poses a very significant challenge? It would be compelling to behold the Google­translated notebooks by Fyodor Dostoyevsky that abound in abbreviations, nonce­words, obsolete words and emphatic structures, or any other text, for that matter, endowed with authorial connotations and concepts, which defy unequivocal interpretation.

It is safe to assume that a translator’s trade will invariably be forced to undergo transformations; that a translator will arguably begin to resemble the famed Blade Runner from the eponymous iconic film by Sir Ridley Scott, that is, they will have to become “more human than human,” ultimately turning into a sort of a blend of Man and Machine. Still, whilst androids and omnipotent AI algorithms have not yet altogether overpowered the world at large, the vocation of a translator qua bearer and repository of creative consciousness and intuition, which the machines are sorely lacking, will remain in high demand. This is precisely what the paper aims to discuss.

In recent years, with the improvement of machine translation technology, more and more people believe that human translation will lose its living space in the future. Various translation software including Netease youdao, Microsoft Bing, Google translation, Iflytek translation and others emerge in an endless stream, involving more and more languages, which not only improves the accuracy of translation but also arouses concerns about the development prospect of human translation among practitioners. Through studying the language environment of the countries along the route in the context of the One Belt and Road initiative, this paper aims to discuss the future of machine translation and human translation, and focuses on the embodiment of human factors in machine translation, then points out that there are no irreconcilable contradictions between machine translation and human translation, but they complement each other, i. e. the correct combination of these two types of translation will ensure reliable AI translation in the future.

The paper discusses key trends in notarized translation given the transformation of the translation profession due to the digitalization of economics and changes in the legal translation industry in the light of the UTR initiative to implement the institution of court (sworn) translators in Russia. The outcomes of the comparative discourse analysis of the notarized and sworn translation are provided given innovations in digital translation, language samples encompassing the translation agencies’ corporate websites. It is concluded that even in the case of successful integration of neural technologies into the translation profession, the agents of the notarial discourse would still be humans — notaries and / or translators possessing digital competencies.

The paper addresses what apparently claims to be termed as digital literary translation. We have conducted an experiment using different engines to render Shakespeare into Russian. Two iconic Hamlet’s monologues in several versions of digital translation including those provided by neural machine translation tools are considered, their competitiveness assessed. The results obtained allow us to argue that the concept of digital literary translation so far merely indicates the sphere of translation; aesthetics and axiology, implied by literary translation involving retention of the aesthetic value and fascination of the original in the digital version, is a matter to be resolved in the future. Time (as artificial intelligence advances) will show whether digital literary translation will gain the status of the term in its “precious ambiguity” or, fortunately (?), remain an oxymoron.

“Intersemiotic transposition” as a type of intersemiotic translation, known since Roman Jacobson’s works, provides the transition from one system of signs to another. Such a transposition, as a rule, is carried out from the verbal to the visual interpretation, which finds its explicit expression in ballet, sculpture, illustrations for literary works, as well as film adaptations, where traditional types of film translation such as dubbing, voice­over translation and subtitling are widely used. Subtitling, with a certain assumption, can be carried out through the use of machine translation and human post­editing. But what about the interpretation “from the visual to the verbal,” which is characteristic, in particular, of works of visual art as culturally and aesthetically significant objects? Will a machine cope with such a type of translation of statics into dynamics? Research into machine translation of sign language gestures has been carried out since 1977. A robotic hand is used to translate words into sign language gestures (the alphabet into the dactyl alphabet) to help people with hearing disabilities to communicate. The situation gets more complicated when one has to describe the dynamics of “moving pictures” for blind and visually impaired viewers during audiovisual translation (typhlo commentary/audio description). Even people who do not have visual limitations may face some difficulties of interpretation in spite of the explicit verbalization of the characters’ body movements in the film. These and other problems are still to be addressed.

In the 21^st year of the 21^st century we can clearly see the effect of digitalization on all walks of our life, knowledge and experience. In such a way, self­learning neural networks capable of performing simultaneous interpreting become a revolutionary development potentially out­competing a human being in the process. Nevertheless, multiple and mostly unsuccessful experiments with artificial intelligence (AI) in simultaneous interpreting have demonstrated that AI cannot replace human intelligence for the time being. What makes it possible, what retention factor is behind it? Such factors are numerous. One of them is attentional control.

Deep learning advancement of machine translation has resulted in significant improvement of translation quality. Machines are now able to solve problems which have traditionally been known as the human translator’s privilege. Nevertheless, the impressive development of technology should not blur understanding of the essence of linguistically mediated communication and the role of translator in this process. The article focuses on the analysis of the complex functional system, which merges the translator and the machine translation software in the integrated unit aimed at the fulfillment of the communicative task. The transformation of the professional translator’s competence is discussed; the significance of the translator’s pragmatic competences is highlighted.

The article discusses the prospects for the development of digital translation, as well as its advantages and disadvantages. An attempt is made to predict the evolution of machine translation using an example of the interaction of artificial intelligence and a French translator’s practical activities. A comparative analysis of student translations from French into Russian with machine translation options is carried out.