Adam Rice

My life and the world around me

Category: language (page 2 of 3)

Denotation vs connotation

Out near Johnson City, there’s a new development going in called “Tierra Mañana.”

Consider the impression this phrase creates, and how very different it is from that of “Tomorrowland.”

Alfresco Whispers

Post-move, I’ve been cleaning out some old papers, and found this. I’ve decided to type it up and post it online for the benefit of future generations. This was originally typed up (and orchestrated) by Chris Poole. Although I’ve tried very hard to reproduce this in exactly the same form as Chris typed it up, it’s quite possible that I’ve introduced a few typos.

I don’t remember exactly which one of these I translated, but it was somewhere in the late 30s/early 40s.

At the closing luncheon of IJET-4 an exercise in consecutive translating was conducted, drawing on the expertise of the assembled translators and interpreters. A simple phrase in English was chosen as the starting point and a Japanese speaker was asked to translate it. This in turn was translated back into English, and then back into Japanese again and so on. People were asked to translate into their own language and were given sixty seconds to do so. No one saw anything but the previous version, and were therefore unaware of the subtle changes that were taking place.

It should be noted here that some difficulty was encountered due to people’s handwriting, but as the participants became aware of the overall objective, a guarantee of anonymity seemed to become more important. In deference to these numerous requests I therefore present the results typed up, with annotation where appropriate.

  1. Bridges between cultures are built on foundations of tolerance.
  2. 文化のかけ橋、忍耐を土台となる。
  3. Patience, indeed, is the foundation of bridges between cultures.
  4. 文化のかけ橋になるのは、忍耐しかありません。 “Foundation” component of metaphor disappears.
  5. The only cultural bridge is forbearance 忍耐 alternatively translated as “tolerance,” “patience” and “forbearance”. The latter perhaps confusing the translator, who finds refuge in an ambiguous use of the word 理解 which then of course becomes “understanding”. A very durable concept which lasts until 21.
  6. 文化は他を理解することで結ばれる。 “Bridge” metaphor disappears via 結び and “link”.
  7. Cultures are linked by understanding others.
  8. 他の人たちを理解することにより文化交流がなされる。 “People” are introduced through the ambiguity of 他.
  9. Cultural exchange is done by evaluating other people.
  10. 文化交流は、外国の人を理解することから始まる。
  11. International understanding begins with an understanding of foreign people.
  12. 国際理解は外国の人を理解するから始まる。
  13. International understanding begins with an understanding of foreign people.
  14. 国際理解は外国人を理解するから始まる。
  15. International understanding begins with the act of understading foreigners.
  16. 会得する、理解、始めに、その行動は外国人の行動を理解すること。
  17. Understand first that behavior is to understand the behavior of foreigners. Statement becomes rather incoherent imperative due to confusing layout of 16.
  18. 外国人の行動であるとまず理解すること。 Does not read 17 as imperative.
  19. To understand from the outset that this is the way foreigners behave. Seems to become conditional clause here.
  20. 外国の方はこういうふうに行動するものだと初めから理解すること。
  21. You must understand that this is how foreigners behave. Back to the imperative.
  22. 外国の方はこうなさいます。 Then back again to descriptive statement.
  23. This is the way foreigners would do it. “would do it” if what? Do what?
  24. これは外国人がよくするやり方です。 Solves above problem, but introduces question of frequency.
  25. This is what foreigners often do.
  26. 外国がどんあことをよく行いますか? Inexplicably becomes question. Also omits 人, leaving sentence to mean “what sort of things do foreign countries often do?”
  27. What kind of things do they like to do in foreign countries? In order to make sense of the above, invents identity/ies, not necessarily native to the countries, who now have a choice about “what they do”.
  28. その人たちは(かれらは)外国にいったときどんなことをしたいのでしょうか。 Good, if cumbersome, translation that makes it plain that “they” are visitors.
  29. What do you think they might want to do when they go overseas?
  30. 太りすぎたらどう対処すると思いますか。 Handwriting problem. Misreads “overseas” as “overeats”.
  31. If you are too fat, how do you handle the problem? Introduces value judgment on obesity.
  32. 太りすぎていたら、どうそれに対応しますか。 Female translator said she would rather not translate something like this. I emphasised that it was only a game so she obliged (but didn’t see obesity as a problem).
  33. If you were too fat, what would you do?
  34. ふとり過ぎていたら貴方はどうなさいますか。 Renders “you” as 貴方
  35. What will the lord do when he gets too fat? Mistakes 貴方 for 貴族 and renders it as “lord”.
  36. 神は肥りすぎたらどうするか? Reads “lord” as “God”.
  37. What do you do if God is too fat? 37, 39, 43, 47 all manage without a personal pronoun in Japanese. Personal pronounds cause problems on both occasions they appear in 34 and 40.
  38. 神様があまり太っていたらどうしますか。
  39. What would you do if god was too fat?
  40. 神が肥満過多だったら貴方は… Bases vague, open-ended question on condition that God were too fat.
  41. If God were too fat, what would you be? Good logical translation that deduces remainder of question.
  42. 肥りすぎの神様がいったらどう思いますか? Raises question of attitude rather than “being”.
  43. If there is an overweight God, what do you think?
  44. 太りすぎの神様がいるとすればどう思いますか。
  45. What would you think of a fat God.
  46. 太った神様をどう思もいますか。 Rumoured fat God lives!
  47. What do you think of the fat God.
  48. 神様太ったでしょう? Renders simple question as traditional Japanese greeting addressed to God.
  49. You look well God! Good translation.
  50. やあ、元気そうじゃないか! Supreme being departs as “God” is read simply as exclamatory component of greeting.
  51. Hello my lover. You’m be lookin’ fine today (Devonshire) Very ably translated into equivalent dialect.

Mnumerimonic

I knew a woman once with the nickname “Sproidy”–she was given this monicker because the letters on the dial of the phone could be used to spell that. I occasionally find myself using this trick in reverse when I need to invent a numeric passcode or the like–I pick a convenient word, and work out the numbers on a phone dial that correspond to it.

There ought to be a name for words and numbers created using this trick, of using numbers to generate words, or vice-versa. Since these are generally used as an aide-memoire, I kind of like “mnumerimonic.” Another possibility might be “numerinym,” though that would only make sense for to words generated from numbers.

Translate this!

Fellow translators of Japanese know that personal names are all but impossible to translate with certainty unless you can ask the person who owns the name how they prefer to have it romanized. When I’m translating a scientific paper (as I am now), the problem is acute, since there is usually a bibliography packed with Japanese names, but these names can often be tracked down, as the authors occasionally have their own web pages, or have been published before in English. So I spend a lot of time googling for their papers and their names.

One citation in my current job has eight names to track down. Ouch. I googled all the surnames together in the hopes that I’d find some bilingual reference with their names. I did not, but I did find a long listing of papers that included the one I’m looking at. Google helpfully offered to translate the page for me. The results for the names in question are interesting and amusing:

汐 promontory positive, increase mountain reason, Kazuhiro Yamamoto, Hiroshi Kondo 也, Doi 玲 child, Ono Megumi child and Ken under village, Ogasawara Masafumi

The Chinese Room

The Chinese Room, sometimes referred to as the Chinese Box, is a thought-experiment invented by John Searle to debunk “strong AI.”

Searle’s argument is that you put an English speaker in a little room. Slips of paper with Chinese are passed in; the English speaker refers to a huge compendium of rules for analyzing and responding to these slips; he follows these rules, produces new slips in response, and passes them out of the room. To a Chinese speaker on the outside, these would appear to be perfectly reasonable responses to the statements on the slips inside, but (according to Searle) that doesn’t mean that the guy in the room understands Chinese.

Jenny and I have long used the Chinese Room as a metaphor for the translation process in some of our knottier jobs–not so much in terms of our weakness with language but with the field of knowledge. I was recently asked to do a mercifully short job on seismology (about which I know almost nothing) that put me in mind of this. The job contained terms that I don’t know in Japanese, and when I found their English equivalents (or in some cases, what I was guessing to be their English equivalents), I dutifully typed them into my translation with only the most superficial idea what they might really mean. Chinese Room. When we find ourselves in situations like this, we just clench our sphincters and hope that the eventual target audience will know what the hell we’re talking about, because we sure don’t.

But thinking about the original Chinese Room argument (and surrounding debate, which is extensive) is frustrating because it is so perfectly hypothetical. Searle’s point was to create an analog to the Turing Test (digression: I just learned that, quite fortuitously, today would have been Alan Turing’s 92nd birthday) that would show up the absurdity of AI. The problem with his argument is that it’s so procedural, so mechanistic. The idea is that there can be a rote response for every input. (This is pretty much the same problem that machine translation today has.) The Chinese room would probably need to be infinitely large to accommodate all the rule books, and it would certainly take an infinite amount of time to prepare those books.

One of the primary arguments against Searle was that the guy in the room might not know Chinese, but the system (of which he is a part) does know it. OK, Searle responds, suppose the guy memorizes all the rulebooks so he doesn’t need to be in the room anymore: he still wouldn’t know Chinese. Aside from it being an improbable memory feat, I’d argue that yes, actually, he probably would. How can you memorize all those characters and rules for dealing with them without developing some kind of internal model of how the language works? One that would allow you to consolidate all the redundancy that would need to be present in the rule books, etc. Sounds a lot like language acquisition to me. In order for Searle’s argument to work, the human would need to be as dumb as the computer, in which case, he’d be undercutting his own argument anyhow. (Digression: I’ve always been struck by how much native fluency in language is basically a matter of following a script: I noticed in Japan that whole conversations would sometimes follow a script with only one or two decision points along the way–other than that, they were entirely ritualized. But in English as well, there are so many ritualistic utterances used in specific situations, or in response to the last ritualistic utterance, that one could probably pull off a pretty good simulation of English fluency by following a rule book with instructions like “when it’s very hot out, greet people by saying “Hot enough for ya?”. Etc.)

I realize this is a tangent to Searle’s original point, but perhaps it can pertain to AI in some way after all: perhaps what the machines really need to be smart is the capacity for abstraction, induction, and deduction. I know this is what some AI researchers are working on.

Out of pocket

Just over the past few days, I’ve noticed two friends (who don’t know each other) using the phrase “out of pocket” to mean something like “very busy.” This new sense for an old phrase doesn’t seem to pop up as a popular result in Google. I asked one of them where he picked it up and if it had any particular nuance; his reply was “can’t remember,” and “no.”

Anyone out there have any insight on this?

[Later] Apparently Gwen uses this a lot, and I’ve either never been around to hear it or never paid attention (I’m going with the former); she tells me it has the added meaning of “unreachable.”

NYT on Katakana

Pretty good article in the Times today on how katakana is used. It mentions that foreigners of Japanese descent, like Alberto Fujimori and Kazuo Ishiguro get the katakana treatment on their names; what it doesn’t mention is that Fujimori, who pronounces his name Spanish-style, gets his name transliterated into kana as フヒモリ (fuhimori).

But the article overstates the standoffishness of katakana for foreign names. Katakana is used for loanwords in general, and for emphasis, and in that respect, it is very similar in function to italics in English. The fact that foreign names get swept up in katakana styling is not that big a deal.

The story reminds me of an anecdote that a friend told by back in Japan. This friend is of Japanese background, has a Japanese last name, and had been living in Japan for some time. She applied for, and got, a JCB credit card, apparently one of the first foreigners to do so. Now, credit cards in Japan always give the holder’s name in katakana; there would be no way of indicating on the card “we’re putting her name in katakana because it really belongs that way, not because of technical limitations.” So they left her last name off entirely, rather than risk having her be confused for a real Japanese.

Words and Rules

Recently finished reading Words and Rules by Steven Pinker. Very interesting and enjoyable. The book breaks down numerous aspects of the way our brains handle language by looking through the prism of irregular verbs, discussing the etymology of irregular verbs (which I found to be the most entertaining part of the book–I guess that says more about me than the book); showing regularity in irregulars (stink/stank/stunk; drink/drank/drunk) and how irregulars get regularized over time; covering how irregulars work in other languages, especially The Awful German Language, where irregular verbs outnumber regular verbs (calling into question the very notion of regularity); and even delving into the neuroanatomical basis for the problems that some people have conjugating verbs.

At the core of the book, though, he’s looking at two basic models for how we organize language in our heads: a Chomskyite rules-based model that reduces irregulars to a few basic rules, which is remarkable as an academic abstraction, but assumes that children are already doctorate-level linguists at an intuitive level; and a neural-network model that assumes our brains unthinkingly string together sounds without the meaning of the words influencing how we use them, a model that is defeated by Pinker’s favorite pet example, the verb “fly,” which is normally irregular (fly/flew) but gets regularized in the limited context of baseball–“he flied out to left field.”

Wa-oops

A pet peeve of mine is Chinese character tattoos. These are often translations of some sentiment the victim wishes to express in code, but have been translated in a way that probably won’t make sense to a native speaker of Chinese or Japanese. In other cases, they are unidiomatic or just plain wrong.

Take a gander at the two kanji above. The one on the left, 和, is the character for “peace,” popular as a tattoo, on T-shirts, decorative rocks, etc. The one on the right looks exactly the same, but for one crucial stroke. In fact, it is not an actual character at all (near as I can tell), though my first guess was that it means “apricot” (I was close: 杏). It is the one on the right that I saw tattooed on the small of a woman’s back on Sunday.

What’s the correct etiquette in this situation? Should I tell her “Hey, I know you wanted the character for ‘peace’ tattooed on your back, but you wound up with something that sorta looks like ‘apricot'”? Or should I leave her in blissful ignorance, as an inside joke for those of us who know the code?

Later: Apparently other people are writing about this problem too.

Adventures in biomechanical translation

Machine translation (MT) is the bugbear of the professional translator. Machine-assisted translation (MAT) is a more devious, and perhaps more pernicious bugbear. Machine translation takes the translator out of the process entirely; machine-assisted translation makes use of the translator’s expertise to create patterns of source/target sentence pairs, and attempts to extrapolate these patterns through the source text. Translation agencies then use the “match rate” as a way to chisel the translator on payments.

Most of the work that I do is not very amenable to MAT (if I used it at all)–my guesstimate is that most of my jobs would have less than a 10% match rate overall. But the job I’m doing right now would be highly amenable to MAT: it’s programming document where a given sentence may be repeated 50 times, with minor variations in predictable spots.

The job was sent to me as a series of MS Word files, which I manually concatenated into one. Word search/replace tools are relatively limited, but BBEdit has a powerful implementation of GREP. So, after much gnashing of teeth, I managed to export a usable HTML file from Word, and cleaned it up. This in itself could be the subject of an even-more-tiresomely long post, which I will spare everyone from reading, and myself from reliving.

Once I got the file whipped into a shape I could stand looking at, I started working out GREP patterns. Some of these were highly productive–one pass would translate 40 or so sentences. Some would only do the one I was looking at. So I’ve been manually reproducing the MAT process, and getting pretty good at GREP syntax to boot. But as I work on it, there’s always a nagging feeling that if I understood that syntax better, I could produce more generalized patterns that would capture more sentences. The ultimate, of course, would be the hideously convoluted pattern that would be required to translate the entire document in one pass–which starts getting into Chomsky territory.

Postscript: I finished that job. What started out as 28 Word files weighing in at a total of 1.2 MB wound up–when I finished concatenating, exporting to HTML, cleaning up, translating, and compressing with Gzip–as a 17.1 KB file. Amazing.

Older posts Newer posts

© 2017 Adam Rice

Theme by Anders NorenUp ↑