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The Translator’s Extended Mind

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Abstract

The rapid development of natural language processing in the last three decades has drastically changed the way professional translators do their work. Nowadays most of them use computer-assisted translation (CAT) or translation memory (TM) tools whose evolution has been overshadowed by the much more sensational development of machine translation (MT) systems, with which TM tools are sometimes confused. These two language technologies now interact in mutually enhancing ways, and their increasing role in human translation has become a subject of behavioral studies. Philosophers and linguists, however, have been slow in coming to grips with these important developments. The present paper seeks to fill in this lacuna. I focus on the semantic aspects of the highly distributed human–computer interaction in the CAT process which presents an interesting case of an extended cognitive system involving a human translator, a TM tool, an MT engine, and sometimes other human translators or editors. Considered as a whole, such a system is engaged in representing the linguistic meaning of the source document in the target language. But the roles played by its various components, natural as well as artificial, are far from trivial, and the division of linguistic labor between them throws new light on the familiar notions that were initially inspired by rather different phenomena in the philosophy of language, mind, and cognitive science.

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Notes

  1. The U.S. Bureau of Labor Statistics (https://www.bls.gov/ooh/media-and-communication/interpreters-and-translators.htm, Apr 28, 2020) predicts a 19% growth in translation job opportunities between 2018 and 2028, which is much higher than the 5% average growth for all careers.

  2. From now on, by translation I will mean technical (non-literary) “written translation,” as opposed to “oral translation” known as interpreting.

  3. These stages and their approximate timelines are as follows: rule-based MT (1950s–1990s), statistical MT (1990s–2015), and neural MT (2015–present). On the history of rule-based MT, see Hutchins (1986) and Hutchins and Somers (1992). For an authoritative introduction to statistical MT, see Koehn (2010). For a technical review of both approaches as of 2008, see Jurafsky and Martin (2008: Ch. 25). Both Koehn (2010) and Jurafsky and Martin (2008) briefly discuss the history of the field. Poibeau (2017) is a popular account that just barely touches the beginnings of neural MT. Koehn (2020) is a state of the art introduction to neural MT. The history of MT lies beyond the scope of the present paper.

  4. See, in particular, Wu et al. (2016) and Hassan et al. (2018).

  5. ‘CAT’ stands for computer-assisted (or -aided) translation, ‘TM’ for translation memory. In speaking of tools, I will use these terms interchangeably.

  6. See Hutchins (1998), Somers (2003), and Sin-wai (2017).

  7. See, e.g., Sánchez-Gijón et al. (2019); Daems and Macken (2019); Knowles, Sanchez-Torron and Koehn (2019).

  8. For a systematic discussion of the extended mind debates by a leading proponent of the idea, see Clark (2008).

  9. “Anyone who seriously tries to understand what translation is about must come close enough to the philosophical disputes about meaning to feel the heat, if not to see the light,” notes Martin Kay (2017: 49), a prominent computational linguist who greatly contributed to the development of computer tools for human translators early in the process (Kay 1980).

  10. See Kay (1980), Hutchins (1998), Somers (2003), and Sin-wai (2017).

  11. Such as SDL Trados Studio (https://www.sdltrados.com), memoQ (https://www.memoq.com), Wordfast (htpps://www.wordfast.com), Déjà Vu (https://atril.com), OmegaT (https://omegat.org), and many others.

  12. In the simplest case, a term base is a two-column electronic table matching specialized source language words (e.g. tubulaire) and multiword expressions (such as muscle cardiaque) with their target language counterparts (tubular and myocard, respectively).

  13. opus.nlpl.eu/EMEA.php. See Tiedemann (2012).

  14. As well as other assorted suggestions coming from different sources, such as longest substring concordance, fragment assembly, and machine translation plug-ins. More on them below.

  15. Fuzzy match rate is a measure of similarity between the source text and the automatically retrieved TM source language entry.

  16. A disclaimer: some of the EMEA corpus segments used in illustrations here and below were deliberately changed from their original form for demonstration purposes and should not be used for any other purpose.

  17. Which was already mentioned in connection with specialized term insertion. Predictive processing is not just a time-saving trick; it is a cognitively important phenomenon which has received much attention lately (see Hohwy 2013; Clark 2014: Ch. 11). It lies at the basis of interactive and adaptive human–machine translation, the topic of section Sect. 6.2.

  18. https://docs-memoq-com.azurewebsites.net/current/en/Places/project-home-muses.html. CAT tool developers do not reveal the details of their algorithms. But it is safe to assume that memoQ’s Muses and similar auto-suggest functions of other CAT tools are a far cry from the state-of-the-art contextual language generation systems based on neural networks. Since the latter have demonstrated much better text prediction performance than the classical n-gram language models (used in statistical MT, which dominated the field before the advent of neural MT around 2016), CAT tools might benefit from incorporating more advanced AI-driven algorithms into their bilingual search and prediction functions. Unfortunately, this may be difficult to implement in traditional CAT tools on a mass scale (see notes 20 and 25). But recent successful deployment of interactive translation prediction and adaptive machine translation in the framework of “smart CAT tools” appears to be very promising. I return to these developments in Sect. 6.2.

  19. Span pre-translation is a term that was first introduced to describe a somewhat similar process in machine not human translation (see Vandeghinste et al. 2017).

  20. Could fuzzy matches be made more “linguistically aware”? This is a translator’s dream, one among many, that unfortunately cannot be realized without enhancing CAT tools with rather advanced AI features, which is impossible to implement in a uniform and economically reasonable way for over 100 morphologically and syntactically diverse languages supported by the tools. Despite their indispensable role in extending the translator’s mind, CAT tools are, almost paradoxically, language-blind. Exploring more advanced fuzzy match options for a given language pair in a restricted framework of a research project is, of course, a different matter. For interesting recent work along these lines on linguistically aware fuzzy matches, see Vanallemeersch and Vandeghinste (2015).

  21. Such positive feedback between “external” visual representation and “internal” mental linguistic representation is typical of cognitive extensions of the human mind (see, e.g., Clark 2008 and 2014: Ch. 9). We will revisit it in the context of interactive/adaptive human–machine translation in Sect. 6.2.

  22. See e.g. https://www.ncbi.nlm.nih.gov/pubmed/17073606 (Feb 2, 2020).

  23. While both languages are mostly head-initial they diverge when it comes to noun phrases; cf. les cellules transversal vs. transverse cells.

  24. Null strings are usually notated as ε. α = ε corresponds to an insertion and β = ε to a deletion.

  25. (a) CAT tools must run very fast on generally affordable desktop or laptop computers. (b) As already mentioned, CAT tools are language-blind: they apply the same “linguistically unaware” algorithms to over 10,000 supported language pairs, despite the numerous grammatical differences between them. But: (i) the computational power of personal computers has been steadily increasing and its cost rapidly decreasing in the recent years; (ii) larger language service providers are actively working to overcome the algorithmic limitations of CAT tools by customizing them to specific language pairs and integrating them with machine translation; and (iii) some CAT tools already accomplish automatic smart fragment assembly in some cases.

  26. In the sense relevant to cognitive science, epistemic actions are “physical actions that make mental computation easier, faster or more reliable”; they are “designed to change the input to an agent’s information-processing system” by “modifying the external environment [in order] to provide crucial bits of information just when they are needed most” (Kirsh and Maglio 1994; quoted in Clark 2014: 194).

  27. E.g. when it comes to resolving co-reference or word sense disambiguation based on broader knowledge of the world.

  28. The emphasis on the resources is important. It has been repeatedly noted above that CAT tools are language-blind. If so, how can they discharge any semantic responsibilities? The answer turns on drawing a distinction between CAT software, which are indeed language-blind, and bilingual resources (i.e. TMs and TBs), which are not. The software can assume the requisite responsibilities by using the resources as data.

  29. In addition, he has various external sources at his fingertips, such as bilingual electronic dictionaries, online encyclopedias, and translation forums.

  30. See Clark and Chalmers (1998) and Clark (2008).

  31. Cf. our discussion of a creative way to reproduce its ambiguity in Sect. 3.3.

  32. See e.g. Bar-Hillel (1960).

  33. Such as Google Translate, Microsoft Translator, DeepL, KantanMT, and others.

  34. E.g. the SCATE project conducted in 2014–2018 by three Belgian universities (see Vandeghinste et al. 2017, 2019).

  35. First by the developers of TransType (Barrachina et al. 2009), followed by those of CASMACAT (Sanchis-Trilles et al. 2014) and SCATE (Vandeghinste et al. 2019).

  36. Bahdanau et al. (2014).

  37. For details, see Wuebker et al. (2016) and Knowles et al. (2019).

  38. https://www.lilt.com. For details, see Green et al. (2014, 2015).

  39. Such is the nature of the language technologies employed in traditional, “non-smart” CAT tools.

  40. The tools, therefore, fully deserve their portion of epistemic credit for the shared task: “If, as we confront some task, a part of the world functions as a process which, were it done in the head, we would have no hesitation in recognizing as part of the cognitive process, then that part of the world is (so we claim) part of the cognitive process” (Clark and Chalmers 1998: 8).

  41. The architectures currently used in machine translation range from long short-term memory- and gated recurrent units-based sequence-to-sequence encoder-decoder models with attention (Bahdanau et al. 2014) to the more recent transformer models (Vaswani et al. 2017) which, at the time of writing, continue to break all performance records. The “unreasonable effectiveness” of neural machine translation is a topic that deserves separate scrutiny but lies beyond the scope of the present paper.

  42. As suggested by recent behavioral studies (Duyck and Brysbaert 2008).

  43. Such as that implemented in bytepair encoding (Sennrich et al. 2016).

  44. “The neurological mechanisms involved in translating and interpreting are one of the chief known unknowns in translation studies” (Tymoczko 2012: 83). “We do not know much about the cognitive processes involved in the translation task” (Poibeau 2017: 22). “In the vast edifice of [translation and interpreting studies], the neurocognitive room so far amounts to little more than a dark, forlorn attic” (García 2019: 1).

  45. See Alves and Vale (2017).

  46. One limitation has to do with the “inverse problem” known in philosophy of science as underdetermination: a given pattern in a translation product may have been generated by different cognitive processes.

  47. Machine translation has been corpus-based since 1990s. And good translation memories are in essence bilingual corpora often annotated with useful metadata.

  48. Such as BLEU, TER or HTER.

  49. See e.g. Nida (1964).

  50. For a review and critical discussion of TAP-based CTS, see Bernardini (2001).

  51. The term is García’s (2019: Sect. 1.2.1).

  52. See Jakobsen (1999).

  53. Such as those implemented in Translog-II (Carl 2012) and Casmacat (Sanchis-Trilles et al. 2014).

  54. Knowles et al. (2019).

  55. For an overview, see Göpferich et al. (2008).

  56. “There is no appreciable lag between what is being fixated and what is being processed” (Just and Carpenter 1980: 331).

  57. See, respectively, O’Brien (2008) and Doherty et al. (2010).

  58. For some doubts on this score, see García (2019: 15).

  59. See Carl and Kay (2012) and Schaeffer et al. (2019).

  60. The similarity between simultaneous interpreting and “parallel” or “vertical” professional translation is reflected in the terms introduced by CTS scholars to describe the time lag between the source text input and the start of its interpreting in the former (ear-voice span) and the time lag between a fixation on a source word and the first keystroke associated with its translation (see Dragsted 2010).

  61. i.e., typing. Notably, many professional translators are touch typists; they can type into the target window while being fixated on the source window.

  62. See Carl and Kay (2012: 954).

  63. Carl and Kay (2012).

  64. For example, Teixeira and O’Brien (2017) report the results of their study of the cognitive ergonomic aspects of memoQ (see Sect. 2 above) using keylogging, eye-tracking, and screen recording.

  65. Such as Translog II (Carl 2012) equipped with reliable gaze-to-word mapping software.

  66. See García (2015).

  67. In contrast, it is much easier to recreate realistic simultaneous interpreting scenarios for the purpose of ERP and fMRI studies.

  68. See Tymoczko (2012) and García (2019).

  69. Who exemplifies both cultures: initially trained as a scientific translator, he is both a neurolinguist and a translation scholar.

  70. According to some recent data, FT tends to generate greater modulations in Broca’s area and the putamen than BT; translation of sentences and of action verbs elicits more activity in the frontostriatal regions, and translation of other words (and especially concrete nouns) in the temporal-parietal regions. Importantly, some of these distinctions can be observed in the absence of explicit behavioral effects (García 2019: 207).

  71. Cf. García (2019: 40, 209).

  72. See, in particular, Buchweitz et al. (2012), Pereira et al. (2018), and Abnar et al. (2018).

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Acknowledgements

Work on this paper was completed during the author’s “study in a second discipline” (linguistics) in AY 2019–20. The author thanks UGA’s Office of the Senior Vice President for Academic Affairs and Provost, the Franklin College of Arts and Sciences, and the Departments of Philosophy and Linguistics for their support. The author thanks Galia Williams and Konstantin Lakshin for discussions of some issues raised in the paper, and the reviewers for the very helpful comments.

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    Yuri Balashov

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Balashov, Y. The Translator’s Extended Mind. Minds & Machines 30, 349–383 (2020). https://doi.org/10.1007/s11023-020-09536-5

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  • Issue Date:

  • DOI: https://doi.org/10.1007/s11023-020-09536-5

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