Helle Dam Jensen* / Anne Schjoldager** / Tina Paulsen Christensen*** / Kristine Bundgaard****

## Introduction to thematic section: Challenges to the perfect machinetranslation situation

## 1. Introduction

'Can machine translation fully dominate and replace human translators?'

'As a freelance translator and a multilingual speaker, I would definitely say no. (…) Machine learning is getting better and better as the industry around it keeps moving forward but it would take a long long time for these machine based translation tools to understand the contexts of our conversations, our songs etc. I even think that these machines can NEVER be as good as a human in identifying which context this line, this sentence or this phase is about. (…) Machines take things too literally'. Duy Thanh (retrieved 10 th  of November 2023)

'AI consistently outperforms humans at chess, Go, driving cars, diagnosing cancer, but when it comes to translation and interpreting, the most sophisticated technology on earth is still by far the human brain.

1. Subjectivity - AI works best when it can apply data + algorithms rooted in objective reality.  Language  is  a  series  of  subjective  conventions  created  by,  used  by,  and constantly updated by humans.

2. Humor and Culture - (…) There are very few well-translated corpuses of humor, wit, sarcasm, and cultural nuance; its hard enough for human translators to get these across  the  language  barrier;  it's  almost  impossible  for  machines.  (…).'  Jonathan Rechtman (recuperated 10 th  of November 2023)

The purpose of the thematic section is to gauge the temperature of MT today by tapping into a selection of critical discussions, thereby shedding light on some challenges to a perfect machine-translation (MT) situation. The advancing MT technology certainly calls for critical discussions.  This  is,  for  instance,  indicated  by  two  translators'  answers  to  the  abovementioned question posed on the online question-answer hub Quora. Both translators reflect on the same issue: MT cannot replace the human translator when the source text is highly context  dependent,  as  the  translation  of  such  texts  tends  to  involve  deviations  from  the

*  Helle Dam Jensen School of Communication and Culture Aarhus University hed@cc.au.dk
- *** Tina Paulsen Christensen School of Communication and Culture Aarhus University tpc@cc.au.dk
- ** Anne Schjoldager School of Communication and Culture Aarhus University asc@cc.au.dk

**** Kristine Bundgaard Department of Culture and Learning Aalborg University kbundgaard@ikl.aau.dk wording  of  the  source  text.  A  case  in  point  are  literary  texts,  which  are  often  deemed problematic for MT because the texts are creative (O'Brien & Ehrensberger-Dow, 2020; Guerberof-Arenas & Toral, 2022) and non-repetitive (Hartley, 2009). In extension, Toral and Way (2018, pp. 264-265) point out that translations of literary texts  are expected to convey  the  reading  experience  of  the  source  text  as  well  as  its  meaning,  and  this  is challenging for MT.

Wrong rendering of content and style are but some out of a plethora of reservations to the quality issues in connection with MT. Others pertain to, for instance, the conception of the nature of translation, societal impacts, ethics, and legal ownership. From a conceptual and also societal point of view, with a reference to O'Thomas (2017), Tieber (2022) points out that when MT takes over translation processes from the human translator, our understanding of translation and translation quality alters, creating an incongruity between the industry and translation scholars, who may have different concepts of translation quality. In the words of Tieber (2022): 'As MT developers strive for MT quality to achieve 'human parity' they create  their  own  understanding  of  translation  quality  and  migrate  the  very  concept  of translation into the realm of machines (O'Thomas, 2017)'. Along the same lines, O'Thomas (2017)  points  out  that  a  further  concern  may  be  that  digitalization  of  translation  may negatively affect the natural development of languages, which evolve 'both in relation to other languages and in response to new contexts as they arise (…).' A related concern has to do with the effect that the potential eradication of human translation will have on human cognition. It can be assumed that MT's gradual takeover of translation tasks will inevitably not  only  diminish  human translation  competences,  but  will  also  impact  human language competences and change society in general.

A different concern from a societal point of view has to do with so-called minor languages (sometimes referred to as low-resource languages; see, for example, Klimova et al., 2022) being disadvantaged when it comes to MT, which may be referred to as a linguistic bias (Bella, 2023). Forcada (2020) defines minor languages as languages with a small number of (literate) speakers, a reduced presence on the internet, several writing and spelling systems, a reduced number of linguistic experts, a lack of computer-readable resources, and a degree of distance from 'normality' (e.g., because the language is mostly used in homes rather than in  public  life).  Forcada  (2020)  lists  a  number  of  possible  challenges  for  MT  with  such languages, mentioning, for instance, that some minor languages lack standard writing and spelling systems and that commercial MT systems are entirely geared for major languages, which are more profitable. Along similar lines, Klimova et al. (2022) outline a number of differences for MT with regard to major/minor languages and MT, including the fact that text-data corpora for major languages are vast, while minor languages are far from wellrepresented in this respect, which creates a huge quality gap in MT output.

Another major issue of concern is ethics. When translation is  passed from the human translator to the machine, translation depends on the data on which the language models are trained.  This  has  led  to  an  increased  interest  in  so-called  algorithmic  bias,  meaning  that machine learning algorithms tend to reproduce biases in the training data, for instance, in relation to gender (Farkas & Németh, 2022). This calls for a discussion of how MT can be carried out in a responsible way. Moniz and Parra (2023, p. 3) characterize responsible MT as 'a combination of all factors that need to be considered when developing and deploying MT systems to ensure that such systems are ethically designed, including, but not limited to, data  bias,  data  licences  and  rights,  ecological  footprint,  and  intended  end-users.'  This, among other things, concerns the data that are used to train MT systems, which may produce a distorted representation of reality by prioritizing certain social groups to the detriment of others, for example with regard to age, gender, ethnicity, and ideology, which are some of the examples listed by Moniz and Parra (2023).

Another  issue  with  regard  to  data  concerns  their  legal  status.  Lacruz  (2023,  p.  74) mentions areas of uncertainty regarding legal rights to mechanical machine translations and the use of translations as training data for MT algorithms. Ownership of translation databases (translation  memories)  has  been  put  into  question  (Topping,  2000),  as  has  the  reuse  of parallel data. With regard to the latter, Moorkens and Lewis (2020) argue as follows:

'Parallel data is repurposed in ever-increasing amounts, but broken down to word and subword levels.  At  present,  rights  to  ownership  are  rarely  passed  to  the  translator, meaning  that,  while  an  initial  translation  may  be  costly,  secondary  uses  are  very inexpensive.'

Additionally, Moorkens and Lewis (2020) point out that the translators in question are not likely to have foreseen the various ways in which MT can be used.

We would like to emphasise that, during the process of preparing and publishing this thematic section, language technology in general has seen new and emblematic developments with the advent of generative pre-trained (GPT) models. As this technology is based on an interaction between a large language model (LLM) and humans via prompts, it  is  possible, at least to  some extent, that the purpose and target audience are taken into consideration by the technology (Yamada, 2023), thus, potentially, reducing some of the MT challenges investigated in this special section. However, it should also be stressed that some challenges remain, for example bias and legal rights to data, and that that new challenges have arisen, such as problems with detecting plagiarism in exam papers and the generation of false data (data hallucination; Martino et al., 2023), leading to misinformation that may be  used  for  propaganda  (Feurriegel  et  al.,  2023).  With  the  use  of  LLM,  translation  and language production in general thus constitute an important and critical research field.

The thematic section contains four articles which address some of the challenges to a perfect MT situation briefly sketched above.

## 2. MT and context dependent language

According to the statements quoted in Section 1, MT of source texts that, in some way or other, depend on context is particularly prone to errors. One way of manifesting context dependency is through expressions that refer to cultural phenomena that are specific to a particular language community. In such cases, the translator functions as a cultural mediator, choosing the appropriate strategies and words (Katan, 1999/2004, p. 214). It can be argued that the translator also takes on the role as a mediator when it comes to translation of other types  of  context  dependency,  for  example,  humour,  style,  idiomatic  expressions,  and idiosyncrasies. These phenomena can involve creativity challenges similar to those sketched in  Section  2.  The  article  by  Schjoldager  et  al.,  'The  professional  translator  vs.  Google Translate:  the  case  of  Lars  Larsen's  autobiography',  contributes  to  the  discussion  by comparing the published (entirely human) translation from 2004 of the autobiography by the Danish entrepreneur and businessman Lars Larsen with a recent MT carried out by Google Translate.  Taking  their  point  of  departure  in  Nord's  (2018)  classification  of  translation problems, the authors analyse three (potential) problem types: situation-related translation problems (often due to cultural references), style-related translation problems (due to register and dialect), and language-related translation problems.

## 3. MT and creativity

When linguistic expressions do not have a direct equivalent in the target language, translation may require a creative solution, that is,  a  solution  that  exhibits  novelty  and  value  to  the context in question (Guerberof-Arenas & Toral, 2022, p. 185 1 ). Such solutions are relevant to the translation of texts that, themselves, can be called creative. Hadley et al. (2022, p. 6) explain that a creative text is highly idiosyncratic and relies heavily on aesthetics. Examples of creative texts are philosophical works, films, advertisement, and literature (Hadley et al., 2022,  p.  6).  Such  texts  do  not  allow  a  direct  translation,  since  this  would  not  lead  to  a functional translation. Exemplifying this, Asimakoulas (2016) argues that the translation of comics often calls for creative solutions, making the translation an integrated part of the adaptation process, rather than just a 'mechanical step'. 2 In sum, creativity is an obstacle to a  perfect  MT,  where  MT  systems  generate  translations  without  human  intervention (Christensen et al., 2022, p. 17). In their article in this section, 'Creative skills development: training translators to write in the era of AI', Guerberof-Arenas and Asimakoulas assume that, although MT is competitive with the human translator when the source text is 'standard and simple', MT cannot outmanoeuvre the human translator when creativity is needed to obtain a functional target text. When it comes to teaching translation, focus must therefore be  on  the  development  of  divergent  and  convergent  thinking.  On  the  assumption  that translation  and  creative  writing  share  common  ground  and  that  competences  in  creative writing can be used productively for translation, they discuss methods for teaching creativity in the classroom.

## 4. MT and gender bias

In the realm of ethics, an ever more pressing concern is that MT, generated on the basis of recycled texts, may propagate discrimination of individuals in a systematic, unjust and biased way  (Friedman  &  Nissenbaum,  1996).  Ullman  (2022,  p.  125)  points  out  that,  although guidelines  to  the  purpose  of  avoiding  imbalanced  training  datasets  have  indeed  been developed, specifications are still lacking as to the effect that actual bias may generate as well as incentives to increase the use of balanced datasets. A much-debated example of bias is  gender. Savoldi et al. (2021, p. 845) list a number of examples of gender biases with language  technologies,  such  as  default  use  of  masculine  pronouns  and  reproduction  of gender  stereotypes  with  regard  to  professions:  feminine  for  nurses  and  masculine  for engineers. In the article 'Good, but not always Fair: An Evaluation of Gender Bias for three commercial Machine Translation Systems', Piazzolla, Bentivogli and Savoldi investigate the level of bias of three commercial MT systems: Google Translate, DeepL, and ModernMT in translation from English into three different languages, viz. Spanish, Italian, and French. To this purpose, they use the dataset MuSTSHEm, composed of spoken language data.

1  See central references to Creativity in Guerberof-Arenas and Toral's (2022) literature review.

2  Asimakoulas (2016) suggests a model for the transfer of classical comedies into comic books and the subsequent translation of these.

## 5. Post-editing and copyright

Closely  linked  to  ethical  concerns  are  uncertainties  with  regard  to  legal  ownership  of machine-translated  texts.  When  content  is  recycled,  for  example  in  text  corpora  and databases,  it  reaches  a  new  purpose.  This,  obviously,  raises  a  number  of  questions  with regard to ownership. When it comes to books, songs, or films, for instance, ownership is protected by the Berne convention (WIPO), conferring to authors the rights to decide who can use their work and for what purpose. However, being based on text corpora made up of texts  written  by  others,  MT  faces  a  number  of  copyright  issues. 3 First,  the  fact  that  the monolingual/parallel  texts  making  up  the  database  for  MT  were  created  for  a  different purpose raises the question if authors and translators should receive compensation (Forcada, 2023, p. 50). Second, the fact that copyright regulations were conceived with a view to the human  originator  does  not  take  into  account  that  human  intervention  in  the  translation process may decrease, resulting in situations where human intervention is null (Lacruz, 2023, pp. 74-75). This generates a need for reassessing the concept of authorship, which seems to imply that authorship can only be conferred to human originators (Miernicki & Ng, 2021 in Lacruz, 2023, p. 76). An area where human intervention is indeed required is in the case of MT of literary texts, as evidenced by the outline above. The fourth concern is, therefore, which  level  of  post-editing  is  required  in  order  to  confer  copyrights  to  the  post-editor. Koponen et al. (2022, p. 191) argue that the post-editor could 'claim ownership' depending on the degree of originality left in the post-edited text, which is in accordance with a report from the European Commission (2014, p. 3) stating that a personal mark from translators may result in copyright. From the point of view of post-editors, this calls for a discussion of what it takes to leave a mark and how post-editors themselves understand their rights etc. It is this latter point of interest that is the focus of the article 'Literary Post-editing and the Question  of  Copyright'  by  Taivalkoski-Shilov  and  Koponen . Zooming  in  on  issues  of originality,  creativity,  and  ownership  in  the  post-editing  of  literary  texts,  they  touch  on aspects of MT that are closely related to the other contributions to the section. To the purpose of shedding light on post-editors' perception of their own role viz-a-viz the target text, the authors analyse peritexts (cover, preface, notes, etc.) and epitexts (interviews with the author, for instance) in connection with MT of two literary texts.

## References

Asimakoulas, D. (2016). Aristophanes in Comic Books: Adaptation as Metabase. Meta

, 61(3), 553-577.

- Bella, G., Helm, P., Koch, G. & Giunchiglia, F. (2023). Towards Bridging the Digital Language Divide. https://arxiv.org/pdf/2307.13405.
- Christensen,  T.  P.,  Bundgaard, K.,  Schjoldager,  A.,  &  Dam  Jensen,  H.  (2022).  What  Motor  Vehicles  and  Translation Machines  Have  in  Common-A  first  step  towards  a  translation  automation  taxonomy. Perspectives.  Studies  in Translation Theory and Practice , 30(1), 19-38.
- European Commission (2014). Translation and Intellectual Property Rights: Final Report . Luxembourg: European Union.
- Farkas,  A.  & Németh,  R.  (2022).  How  to  measure  gender  bias  in  machine  translation:  Real-world  oriented  machine translators, multiple reference points. Social Sciences & Humanities Open , 5(1), 1-11.
- Feuerriegel,  S.,  Hartmann,  J.,  Janiesch,  C.  &  Zschech,  P.  (2023). Generative  AI.  Business  &  Information  Systems Engineering, https://doi.org/10.1007/s12599-023-00834-7.

3  These are just a selection out of a series of challenges to copyright. See, for example, Forcada (2023) and Lacruz (2023) for a detailed overview and discussion.

- Forcada, M. L. (2023). Licensing and usage rights of language data in machine translation. In H. Moniz & C. Parra Escartín (Eds.), Towards responsible machine translation: Ethical and legal considerations in machine translation (pp. 49-69). Springer.
- Forcada, M. L.  (2020).  Building  machine  translation  systems  for  minor  languages:  challenges  and  effects. Revista  de Llengua i Dret, Journal of Language and Law , 73, 1-20.
- Friedman, B. & Nissenbaum, H. (1996). Bias in computer systems. ACM Transactions on Information Systems (TOIS), 14(3) , 330-347.
- Guerberof-Arenas, A., & Toral, A. (2022). Creativity in translation: machine translation as a constraint for literary texts. Translation Spaces , 11(2), 184-212.
- Hadley, J.L., Taivalkoski-Shilov, K., Teixeira, C.S.C. & Toral, A. (2022). Introduction. In J.L. Hadley, K. TaivalkoskiShilov, C.S.C. Teixeira, & A. Toral (Eds.), Using Technologies for Creative-Text Translation (pp. 1-17). Routledge.
- Hartley, T. (2009). Technology and translation. In Munday, J. (Ed.), The Routledge companion to translation studies (pp. 106-127). Routledge.
- Katan,  D.  (1999/2004). Translating  Cultures,  An  Introduction  for  Translators,  Interpreters  and  Mediators .  Jerome Publishing.
- Klimova, B., Pikhart, M., Benites, A.D. Lehr, C. & Sanchez-Stockhammer, C. (2022). Neural machine translation in foreign language teaching and learning: a systematic review. Education and Information Technologies .
- Koponen,  M.,  Nyqvist,  S.,  &  Taivalkoski-Shilov,  K.  (2022).  Translating  with  technology:  How  digitalisation  affects authorship and copyright of literary texts. In J. L. Hadley, K. Taivalkoski-Shilov, C. S. C. Teixeira, & A. Toral (Eds.), Using Technologies for Creative-Text Translation (pp. 180-198). Routledge.
- Lacruz Mantecón, M.L. (2023). Authorship and Rights Ownership in the Machine Translation Era. In Moniz, H., Parra Escartín, C. (Eds.), Towards Responsible Machine Translation. Machine Translation: Technologies and Applications, 4 . Springer, Cham.
- Martino,  A.,  Iannelli  M.  and  Truong,  C.  (2023).  Knowledge  Injection  to Counter  Large  Language  Model  (LLM) Hallucination. In C. Pesquita et al. (Eds.), The Semantic Web: ESWC 2023 Satellite Events . ESWC 2023. Lecture Notes in Computer Science, 13998. Springer, Cham.
- Miernicki, M., Ng, I. (Huang Ying) (2021). Artificial intelligence and moral rights. AI & Society , 36, 319-329.
- Moniz, H., Escartín, C.P. (2023). Introduction. In H. Moniz & C. Parra Escartín (Eds.), Towards Responsible Machine Translation. Machine Translation: Technologies and Applications, 4. Springer, Cham.
- Moorkens, J. & Lewis, D. (2020). Copyright and the Reuse of Translation as Data. In M. O'Hagan (Ed.), The Routledge Handbook of Translation and Technology (pp. 469-481). Routledge.
- Nord, C. (2018). Translating as a purposeful activity: Functionalist approaches explained . Routledge.
- O'Brien, S. & Ehrensberger-Dow, M. (2020). MT Literacy - A cognitive view. Translation, Cognition & Behavior , 3(2), 145-164.
- O'Thomas, M. (2017). Humanum ex machina: Translation in the post-global, posthuman world. Target, 29(2 ), 284-300. Quora (n.d.) [online].

https://www.quora.com/Can-machine-translation-fully-dominate-and-replace-human-translators?top\_ans=241036765

- Savoldi, B., Gaido, M., Bentivogli, L., Negri, M. & Turchi, M. (2021). Gender Bias in Machine Translation. Transactions of the Association for Computational Linguistics , 9, 845-874.
- Slessor, S. (2020). Tenacious technophobes or nascent technophiles? A survey of the technological practices and needs of literary translators. Perspectives , 28(2), 238-252.
- Tieber, M. (2022). Perfect MT and the Concept of Translation . Abstract HAL panel at NeTTT Conference, 4-6 July 2022, Rhodes Island, Greece.
- Topping, S. (2000). Sharing translation database information: Considerations for developing an ethical and viable exchange of data. Multilingual Computing and Technology , 11(5), 59- 61.
- Toral,  A.  &  Way,  A.  (2018).  What  Level  of  Quality  can  Neural  Machine  Translation  Attain  on  Literary  Text?  In  J. Moorkens, S. Castilho, F. Gaspari & S. Doherty (Eds.), Translation Quality Assessment. From Principles to Practice (pp. 263-287) Springer.
- Ullmann, S. (2022). Gender Bias in Machine Translation Systems. In Hanemaayer (Ed.), Artificial Intelligence and Its Discontents, Social and Cultural Studies of Robots and AI (pp. 123-144). Springer Nature.
- World Intellectual Property Organization (WIPO) (n.d .): Berne Convention for the Protection of Literary and Artistic Work [online]. https://www.wipo.int/treaties/en/ip/berne/.
- Yamada, M. (2023). Optimizing  Machine  Translation  through  Prompt  Engineering:  An  Investigation  into  ChatGPT's Customizability. https://arxiv.org/abs/2308.01391.