A seven-volume dictionary, A Magyar Nyelv Értelmező Szótára, just arrived from Hungary. 23 pounds, 1.4 ounces, 7362 pages.
I decided to test-drive it on a sentence from Ida, the book I used in my Translation Exercise #1: “Egy pohos úr a Kávékirályban göcögve nevette.” Neither “pohos” nor “göcög” is in my iPad dictionary, but my big Hungarian-English dictionary defines “pohos” as “pot/big-bellied, paunchy”. Even my best dictionary didn’t have “göcög”, but Googling turned up an 1897 dictionary which defined it as “magába fojtva nevet”, and my iPad dictionary does have “fojt”, which it defines as “choke, stifle, suffocate”, and “magába fojtja érzelmeit”, “repress/supress one’s feelings, bottle up one’s feelings”, so I was fairly comfortable concluding that “göcögve nevette” could be translated as “stifled laughter”.
The new dictionary basically agrees on “pohos”, with the note that the word is “kissé rosszalló v. gúny” (slightly derogatory or derisive). For “göcög”, though, the definition is “(kisgyermek, kövér ember) jóízűen kacag, hogy a teste is rázkodik; döcög (5)”, with the sentence I quoted from Ida used as an example. This is exactly the opposite of what I thought it meant: “(small children, obese person) laugh heartily, so the body also shakes”. To be safe, I also looked up the fifth definition of “döcög”, which reads in part “teste rázkodik a nevetéstől”, “the body shaking from laughter”, and also “el-elfulladva, szakadozottan beszél”, which basically means that you’re laughing so hard you can’t speak or breathe.
Presumably, the definition which led me astray should have been interpreted as “laughing so hard you choke” (i.e., can’t breathe). The more complete definition is harder to misinterpret, so I think I can safely conclude that this purchase was well worth the money.
Oh, and the translation of that sentence, in context:
A stout gentleman in the Coffee King laughed heartily, his enormous belly rippling:
“The devil to these newspaper writers!” he said, throwing down the paper. “What great villains!”
Estonian has no word for “bread”. This can pose a problem for the unwary translator. In particular, it can pose a problem for the makers of a flashcard program I bought, who translated the word “bread” as “leib” and illustrated it with a photograph of a loaf of sliced white bread.
“Leib” is the Estonian word for black bread. The Estonian word for white bread is “sai”. But if you look up bread in a typical English-Estonian dictionary, it may not explain that distinction. When an Estonian sees the English word “bread”, he thinks of leib—that’s what they eat. He knows, intellectually, that the word also encompasses sai, but the concept of bread as an American understand it, of leib-and-sai, doesn’t come to mind as readily or as naturally to an Estonian. So the poor maker of the flash card program, who is after all just adapting the same English words and photos to dozens of different languages, looks up “bread” in the dictionary and gets the wrong answer. Or maybe he gives a list of words to be translated to an Estonian consultant, but without photos attached, and gets the wrong answer. And even if he got the right answer, he won’t be able to convey that right answer on a flashcard.
Conceptually, flashcards are based on the presumption that a word in one language has a unique translation into another language. That presumption is a useful approximation, in the same way that a spherical point mass is a useful approximation of a cow. And understanding why that presumption often fails illuminates why good machine translation is so difficult, and so far off. After all, simply to translate a sentence containing the word “bread” into Estonian requires information that may not be in the text—anywhere.