Translation Diary: Daniel Levin Becker on Laurent Mauvignier's "The Birthday Party" (Part 2)
Second pass: Walruses into space
So now the manuscript is in English. A teetering, warty, lacunary English, but English all the same. The second pass is more satisfying than the first but no less grueling, not least because all the questions and confusions I cheerfully pushed off the first time through now come home to roost. What I’m creating this time is not a text transposed into a new language but a book, which means I’m responsible not only for what the words mean but also for the spaces between them, for the way they fit together or, as the case may be, don’t—and, if not, why not. I have to think like a writer.
And Mauvignier is a writer. Everything in The Birthday Party is deliberate and precise, even its imprecision, even its curious word choices, even the long and tortuous sentences unbothered by conventional methods of delivering or sequencing information. Everything is engineered to create an effect that both embodies and commentates the micro-moment at hand—an ellipsis here, a demotic clipping there, a stream of consciousness that doubles back without warning and picks up a thought abandoned pages before—all without breaking the story’s overpowering momentum. My task is to imitate that mastery without smoothing it into banality or, conversely, overplaying the strangeness of craft that comes when you read a sentence so closely that you’re aware of its every joint and seam.
“Everything in The Birthday Party is deliberate and precise, even its imprecision, even its curious word choices, even the long and tortuous sentences unbothered by conventional methods of delivering or sequencing information.”
Naturally it can be difficult, as a non-native speaker of the source language, to know where exactly to draw that line: what’s wobbly but intentionally so, what’s intentional but wobbly all the same, what seems out of place until you identify the remote fragment of thought it’s resuming, what’s just an idiom I’ve never encountered before. Happily, I have an excellent resource in my friend Nicolas, an English-to-French translator who’s always game, in exchange for the equivalent service in the opposite direction, to entertain my queries and give me either a clear ruling or a serenely Confucian non-answer. “Yes,” he’ll say when I manage to paraphrase a sentence whose logic I’m only just beginning to fathom: “that’s what he means, but it’s not what he’s saying.” Or: “You’re right, it’s a bit awkward to phrase it that way. But it’s a good awkward.”
Such skirmishes with the author’s idiosyncrasies add up, especially over 640 pages, but just as often my antagonist in the second pass is the translator, which is to say myself a month or two ago. Accordingly the majority of the work is correcting my own garden-variety errors, the best of which is from a scene where a character recalls a psychotic episode of trying to communicate with aliens by Morse code, which in my haste I rendered—and honestly don’t remember rendering—as sending walruses into space. Or, if not outright errors, then places where I was initially content to take cognates at face value. Cognates are words that look alike between two languages, and false cognates—the French call them faux amis, or false friends—are the scourge of good-faith attempts at mastering foreign vocabulary. Table means table, but chair means flesh.
Revisiting and revising this manuscript, I found myself almost constantly changing adjectives and verbs to look less like their French counterparts: upon reflection, résonner was not to resonate but to reverberate; inconcevable was not inconceivable but incomprehensible. I personally find it great fun to dwell on the etymological roots that connect connotations a nuance or two apart, and perhaps this is another defense of the poor inefficient human in the age of machine translation: an algorithm can inventory all the attested ancillary senses of this or that word in the blink of an eye, but good luck getting it to decide which one is right in a given context. It can scan a corpus of existing texts and rank how frequently they’ve appeared adjacent to the other relevant words, sure, but isn’t bucking that kind of predictability one of the literary writer’s most cherished aims?
Continue reading: Part III.
