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Home > Archive > Article: Hans Verghese Mathews
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Borges and Machine Translation:
'Blue Tigers'
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Translation by Hans Verghese Mathews

This is a translation of a story ‘Blue Tigers’ by Jorge Luis Borges. As hitherto done with previous translations in Phalanx the actual translation is provided through a link to a PDF. But before the actual story is made available this exercise begins with a note from the translator reflecting on the experience of translating Borges online using the different tools available although the actual translation is done in the normal old-fashioned way.

A story by Borges in an issue given over to AI seems out of place ―― more so that it should be Blue Tigers, Tigres Azules, one of the very last and more strange among the ficciones  ―― and that the editors should have indulged themselves so will seem only laziness, moreover, to aficionados; who, even if they think the phrase ‘artificial intelligence’ an oxymoron, could point to more pertinent writing in the oeuvre; to Borges’s divagations on the characteristica universalis projected by Leibniz, for instance, or to his singular Defense of the Kabbalah even.

Some excuse should be offered; or attempted at least; and an opening of sorts  ―  a chance chink, not a “window of opportunity” ―  is provided by the ending of Tigres Azules: the englishing of its last sentence presents a peculiar obstacle, as it happens, to machine-translation out of Spanish into English. Defending such a claim would not be easy ―― to advance a comprehensive argument in any economical way is beyond me  ―― and I shall only try to suggest why.

So that there will not be a riot of quote-marks I shall use the courier font from now on, rather than single-quotes, to refer to words or phrases within sentences:
to “mention” rather than “use” them, as philosophers of language used to say. But I shall keep on using double-quotes in the usual ways: either to quote verbatim or, else, to hedge assertion.

No oí los pasos del mendigo ciego ni lo vi perderse en el alba: our ficcion closes just so, upon a sentence set off by itself. Our translator has hazarded what an algorithm is likely to discard ―― I did not hear the steps of the blind beggar, nor see him lose himself in the dawn ―― because  perderse  is not functioning as a reflexive pronominal verb in the sentence: which a transcribing algorithm would segment and parse first, assigning “parts-of-speech” to words and phrases, before proceeding. I apologise for the terminology of the grammarians; but resorting so is the shortest way to go, and, besides, the Web will tell curious readers enough about pronominal verbs in Spanish, and in French; and in Latin too, should they be curious enough.

The empirical man will ask if one could ever see someone lose himself any- where: but he will have better things to do, doubtless, than read Borges. There are a good many sites offering automatic translation on the Web, and I thought it prudent to test my guess by passing our sentence to their algorithms. I tried seven sites; and (with the names of the sites appended) here are the English sentences that were returned:

A     No oí  |  los pasos  |  del mendigo ciego  |  ni lo vi perderse  |  en el alba

A1   I did not hear  |  the footsteps  |  of the blind beggar  |  nor did I see him get lost  |  in the dawn

Google Translate
A2   I did not hear  |  the footsteps  |  of the blind beggar  |  nor did I see him lose himself  |  in the dawn

A3   I did not hear  |  the footsteps  |  of the blind beggar  |  nor did I see him get lost  |  at dawn 
A4   I did not hear  |  the steps  |  of the blind beggar  |  nor did I see him get lost  |  at dawn       

A5   I did not hear  |  the footsteps  |  of the blind beggar  |  or see him lose himself  |  in the dawn
A6   I did not hear  |  the footsteps  |  of the blind beggar  |  nor did I see him lost  |  at dawn 
A7   I didn't hear  |  the blind beggar's footsteps  |  or see him get lost  |  at dawn
I have sectioned  A  in the way that all but two of the seven transcribing sentences seem to have segmented the original: A2  and  A5  are the evident exceptions.

The algorithms of DeepL and were not blind-sided by their parsers, it looks, in the way the others seem to have been; for while get lost may be a competent transcription of  perderse  by itself, paying no heed to context, and though  at dawn  for  en el alba  may be passable elsewhere, the rendering   get lost at dawn  dissociates  alba  from  perderse distortingly here: in A   the losing does not happen at dawn merely.
               employs “machine translation technology” and “artificial intelligence” the site says ―― distinguishing Iberian from Latin American Spanish and Standard American from English, reassuringly, in the provided options ―― and that  or  rather than the literal  nor  was put in for ni suggests that its algorithm is more than ordinarily alert to context and usage (if without conspicuous success just now); and it is tempting to say the same of the DeepL routine.

The output of PROMT was tweaked to sound conspicuously colloquial; and, as one might expect, the interface provides a “contexts” button activating an algorithm that, given a string of words, will retrieve actual samples of putatively cognate usage from its database. For  perderse en  it supplied the following sentence together with a translation:
B    En este mundo, es fácil perderse en los engaños y las tentaciones.
B1  In this world, it’s easy to lose oneself in all the deceptions and temptations.
The  all  interpolated in  B1  is surprising (one could excise  all the there without loss) but, all the same, the transcribing routine seems to have sectioned  B  in a natural way: linking  en  to  perderse  as much as to  los engaños y las tentaciones it looks. One wonders why the PROMT algorithm did not do likewise in transcribing A:  as the routines of both DeepL and will now appear to have.

Noticing that the comma in B did no work, really, I thought to experiment a
little more by sending
C   en este mundo es fácil perderse en los engaños y las tentaciones
to DeepL: expecting that its routine would have  en  qualify  perderse  once more, as I supposed it to have done in transcribing  A  with  A2. But what it returned was the less than natural.
C1  in this world it is easy to get lost in deception and temptation.
alas: which indicated, to the contrary, that  en  had been assimilated into the noun-phrase  los engaños y las tentaciones  without reference to the verb perderse.
The next step was to try with  C  in the hope that it would do better than DeepL: but
C2  in this world it is easy to get lost in deceptions and temptations.
was the disappointing result. A conscientious man would have repeated the exercise with the other sites used to translate A, but I did not have patience enough for that (not just then. But a few days after I did try both DeepL and with the final two sentences of Tigres Azules, to see what difference that might make to their transcriptions of the last one: there wasn’t any.)
So, leaving things just so, I shall try now to characterise the “peculiar obstacle” that, as I had asserted, machine-translation from Spanish to English must overcome; and the complication, very summarily, is that Spanish has much richer ways than English to lexically register the modulation of agency in daily doing and common experience.

Here are three suggestive examples of verb and cognate reflexive pronominal:
caer                                    caerse
to fall                                 to drop
negar                                   negarse
to deny                               to refuse
salir                                     salirse
to leave                               to escape
To pursue the matter one would have to consider generally the use of such and like “deponent” verbs in Spanish ― inherited from Latin, and preserved there more than in French maybe ―  the complex semantics and pragmatics of which have been studied quite extensively (so a quick look online suggested) with a view to characterising how their morphology indexes agency medial to and varying between active doing and passive enduring. I shall venture, thereupon, that learning the use of deponent verbs requires a body: somatic proprioception would condition success ―― “inner sensing” must weigh syntactic proximity properly ―― and the discriminations of inner bodily affect are precisely what a successful transcribing algorithm would have had to detect, somehow, sans body, in the disjuncta membra of the corpora it was trained upon. Readers acquainted with linguistics would have pronounced opinions on the possibilities here. The partisans of Lakoff, for instance, are likely to agree that mastering deponents requires a body ―― and would deny, maybe, that an algorithm could sufficiently detect how proprioception conditions utterance (in poetry parti- cularly) ―― but disciples of Chomsky might be more circumspect: even though the master has not been very complimentary about “natural language processing” by computer,  as far as I know (indifferent to literature though he appears to be), and he has scoffed at the “generative AI” that has birthed the seeming wonder risibly named “ChatGPT”.
To say anything more without probing the processing algorithms themselves would be very foolish however ... and an enthusiast for AI would say that my tinkering has proved nothing at all ...  
so I must leave the reader to Borges and his blue tigers now:
venturing this much more only, and improperly maybe, that the man who contrived those estranging fictions was a lyric poet fending, with words, within words, the perditions ― las perdiciónes ― of old age.

Hans Verghese Mathews

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Complete Story: Blue Tigers

courtesy: https:Jorge_Luis_Borges_bibliography

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