//請分清楚這觀念：
若譯文是佳作，請注意是否是畫蛇添足。
大家都這麼做，大家的譯文變成作文，大家都受到尊敬。
請問這樣公平嗎？//
認真嚴肅的譯評，一定會仔細對照原文與譯文，因為這樣才能評斷翻譯的品質。將翻譯當成作文，扭曲原文的意思，塞入原文沒有東西，很容易被人發現，通常只會被人批評，更不會得到尊敬。
但譯文如果加了一點原文沒有的東西，是否一定就是「畫蛇添足」，應受譴責呢？
黃霑〈廣告翻譯，蓄意叛逆〉有一段話頗有意思：
//在我唸中學的時候，我的母校喇沙書院有位翻譯老師袁匯炳先生，他曾在五十年代一人兼得中譯英與英譯中的兩項公開譯詩獎，是我所見第一位以離騷體譯莎翁十四行詩的人。他提出過四項很別致的翻譯方法：“刪、存、補、掉”。他認為譯外文，有些地方要刪去，有些要存下來；意思不能一語直譯的，就要補足，次序不合中文語法的，就要“掉轉”。這未必是一位嚴肅的翻譯家所能同意的方法，但對我這個從事廣告的學生，卻十分適用。我們演繹外國廣告，用的正是“刪、存、補、掉”，不合的刪去，合的保存，不足的補足，次序掉亂得令原來的東西潰不成軍。//
職業譯者為求保存原文完整的意思，通常不能用「刪」這種方法，頂多只能刪去一些微不足道的枝節，以免妨礙譯文的順暢表達。
職業譯者為求保存原文完整的意思，通常不能用「刪」這種方法，頂多只能刪去一些微不足道的枝節，以免妨礙譯文的順暢表達。
同樣道理，為了順暢表達原文的意思，為了方便讀者理解，譯者如果不想用可能干擾讀者閱讀的譯註，可能會適時補一點原文沒有的東西（例如扼要地補一點關鍵背景資料）。如果譯者處理得好，沒有扭曲原文的意思又真的有助讀者閱讀理解，我們或許很難找到有力的反對理由，更不會說這是作文而非翻譯。
翻譯有基本的標準，但或許沒有絕對的標準，例如準確傳達原文的意思是基本標準，但這並不要求我們百分百採用原文使用的字詞和句式（因為兩種語言的差異，這有時根本做不到）。譯得好不好，始終要看譯文的實際整體效果。
至於常常連原文的意思都無法正確理解的譯者，難免常常出現低級的錯誤，這就沒什麼好討論的。
最後補一句：翻譯始終是不大受重視的一個職業，譯者始終不大受人重視，遑論尊敬。
原文作者英文文法寫錯，如何認定翻譯正確標準:
回覆刪除我昨天在網路上尋找 「…某原文書」的作者的聯絡email。終於找到了。我寫 兩封 email 給他，問他關於我翻譯他的書內容所遇到的模棱兩可的問題。其中一個問題與您們正在處理的「試譯」審查有關。
內容如下：(當然我並沒有直接批評這位英國作者的英文文法錯，導致我翻譯時的左右為難的猜測。我用暗示的方式問作者相關問題，結果不出所料，在作者發現了寫錯英文文法的問題，立即修改了和文法的句子給我。)
問題是： 我覺得作者有一個英文句子的英文文法寫錯:
as it is in the hands of those using it to liberate, educate, connect and delight.
這些動詞 liberate, educate, connect, delight 的詞性都是屬所謂的「完全及物動詞」，後面應該要接名詞當受詞。
在作者回覆的內容中，作者修改了這句英文內容，也順便在這些動詞 liberate, educate, connect 後面，新增加名詞 (them)當受詞。這就完全符合了我的文法理論。作者也同意我的翻譯內容是正確的，也是他原本要表達的意思。
所以從現在起，我更有100%的信心，來翻譯原作者的這本書，即使 原作者的英文是不小心筆誤寫錯的或意思模糊。
我詢問作者如下：
Question2: Can you give an explanation of the sentence, “The internet is as powerful a force in the hands of many of the world’s repressive and censorious regimes as it is in the hands of those using it to liberate, educate, connect and delight.”?
For the phrase,” to liberate, educate, connect and delight.”, does it mean “to maintain media freedom, implement equal education, build social platforms, and create entertainment value” ?
作者回答如下：
And in the second, the idea is one that is probably far more obvious today than it was when I wrote those words back in 2011 - that the internet is just as powerful a tool for controlling and oppressing people as it is for liberating, educating and connecting them - I would agree with your phrase “to maintain media freedom, implement equal education, build social platforms, and create entertainment value” as a fair translation of the meaning of the final phrase, yes.
作者新修改的新句子：
The internet is just as powerful a tool for controlling and oppressing people as it is for liberating, educating and connecting them.
原本PDF檔案中的句子:
The internet is as powerful a force in the hands of many of the world’s repressive and censorious regimes as it is in the hands of those using it to liberate, educate, connect and delight.
答覆：雖然原本PDF檔案中的這個句子是錯誤的，但我的譯文是先在心裡分析更正了原作者的句子錯誤，才翻譯成中文的。在作者的email 回覆中，作者也同意這句譯文是正確的，也是他的想法。
請問我應該更改成作者新修改的新句子，然後重新翻譯此句？ 還是維持現狀？
進階到用電腦程式語言來研究翻譯的境界
回覆刪除最近閱讀您們審查「試譯」的回饋內容之後，感覺很有收穫，學到新的東西，今天讓我想起，我之前跟您說的下列提案，其實我從小時候學英文時，就很喜歡”翻譯研究”，現在的我已經把這個翻譯研究的能力，進階到用電腦程式語言來研究翻譯，寫程式教電腦學習翻譯人類的文字，這是一個全新的機器學習科學。一般是使用 python 程式語言，其實 prolog 程式語言曾經紅過一時研究翻譯程式。
剛才重新大略複習一次 Python NLTK對 人類語言的翻譯程式應用，
如下列網站免費 Python NLTK電子書所示，如果我提議寫一本像
“揭發Python NLTK 翻譯人類語言程式的技術” ，您們公司會有興趣嗎？
10. Analyzing the Meaning of Sentences
We have seen how useful it is to harness the power of a computer to process text on a large scale. However, now that we have the machinery of parsers and feature based grammars, can we do anything similarly useful by analyzing the meaning of sentences? The goal of this chapter is to answer the following questions:
1. How can we represent natural language meaning so that a computer can process these representations?
2. How can we associate meaning representations with an unlimited set of sentences?
3. How can we use programs that connect the meaning representations of sentences to stores of knowledge?
Along the way we will learn some formal techniques in the field of logical semantics, and see how these can be used for interrogating databases that store facts about the world.
1 Natural Language Understanding
1.1 Querying a Database
Suppose we have a program that lets us type in a natural language question and gives us back the right answer:
(1)
a.
Which country is Athens in?
b.
Greece.
How hard is it to write such a program? And can we just use the same techniques that we've encountered so far in this book, or does it involve something new? In this section, we will show that solving the task in a restricted domain is pretty straightforward. But we will also see that to address the problem in a more general way, we have to open up a whole new box of ideas and techniques, involving the representation of meaning.
So let's start off by assuming that we have data about cities and countries in a structured form. To be concrete, we will use a database table whose first few rows are shown in 1.1.
How can we get the same effect using English as our input to the query system? The feature-based grammar formalism described in 9. makes it easy to translate from English to SQL. The grammar sql0.fcfg illustrates how to assemble a meaning representation for a sentence in tandem with parsing the sentence. Each phrase structure rule is supplemented with a recipe for constructing a value for the feature sem. You can see that these recipes are extremely simple; in each case, we use the string concatenation operation + to splice the values for the child constituents to make a value for the parent constituent.
>>> nltk.data.show_cfg('grammars/book_grammars/sql0.fcfg')
% start S
S[SEM=(?np + WHERE + ?vp)] -> NP[SEM=?np] VP[SEM=?vp]
VP[SEM=(?v + ?pp)] -> IV[SEM=?v] PP[SEM=?pp]
VP[SEM=(?v + ?ap)] -> IV[SEM=?v] AP[SEM=?ap]
NP[SEM=(?det + ?n)] -> Det[SEM=?det] N[SEM=?n]
PP[SEM=(?p + ?np)] -> P[SEM=?p] NP[SEM=?np]
AP[SEM=?pp] -> A[SEM=?a] PP[SEM=?pp]
NP[SEM='Country="greece"'] -> 'Greece'
NP[SEM='Country="china"'] -> 'China'
Det[SEM='SELECT'] -> 'Which' | 'What'
N[SEM='City FROM city_table'] -> 'cities'
IV[SEM=''] -> 'are'
A[SEM=''] -> 'located'
P[SEM=''] -> 'in'
This allows us to parse a query into SQL.
>>> from nltk import load_parser
>>> cp = load_parser('grammars/book_grammars/sql0.fcfg')
>>> query = 'What cities are located in China'
>>> trees = list(cp.parse(query.split()))
>>> answer = trees[0].label()['SEM']
>>> answer = [s for s in answer if s]
>>> q = ' '.join(answer)
>>> print(q)
SELECT City FROM city_table WHERE Country="china"
出版公司可能利用身分調查表格決定稿酬!
回覆刪除關於出版公司上次給我的作譯者身分調查表格，上面有需要填入作者是否有證照、學歷等等。
建議出版公司可在表格中增加一項，關於作譯者 畢業的學校是否正派經營(例如是否培育出 諾貝爾獎得主，美國總統/副總統等等)。
下列是我的美國母校諾貝爾獎得主 等資訊，欲知詳情網路上可查。
Richard F. Heck
Richard F. Heck, the Willis F. Harrington Professor Emeritus in the Department of Chemistry and Biochemistry at the University of Delaware, received the Nobel Prize in Chemistry on Dec. 10, 2010, in Stockholm.
Heck was honored alongside fellow researchers Akira Suzuki of Hokkaido University in Sapporo, Japan, and Ei-Ichi Negishi of Purdue University, “for palladium-catalyzed cross couplings in organic synthesis.” They shared a $1.5 million award.
According to the Nobel statement, the scientists were honored for discovering “more efficient ways of linking carbon atoms together to build the complex molecules that are improving our everyday lives.”
Palladium-catalyzed cross coupling is used in research worldwide, as well as in the commercial production of pharmaceuticals and molecules used in the electronics industry.
UDaily Coverage (1) (2) (3) (4) | Nobel Prize Coverage | Professor Heck's Lecture: * Award Ceremony: UD Research Magazine
UD Video | (1) In Memoriam | (1)
Daniel Nathans
Daniel Nathans, who graduated from the University of Delaware in 1950, summa cum laude with distinction in chemistry, won the Nobel Prize in Physiology or Medicine in 1978. The prize, which he received alongside Werner Arber and Hamilton Smith, was "for the discovery of restriction enzymes and their application to problems of molecular genetics."
Because his research laid the groundwork for the mapping of the human genome, Nathans is known as "the father of modern biotechnology."
As recorded in John Munroe's The University of Delaware: A History, when Nathans returned to Newark in 1979 to receive an honorary degree at commencement, he singled out six of his former professors to praise, three for their inspiration in his scientific studies -- Arnold Clark (biology), Quaesita Drake (chemistry) and Elizabeth Dyer (chemistry) -- and three for broadening his interests in other fields -- Anna J. DeArmond (English), Felix Oppenheim (political science) and Bernard Phillips (philosophy).
Nathans was inducted into the University of Delaware's Alumni Wall of Fame in 1985. He also returned to campus in 1993 and delivered remarks at the dedication of UD's Lammot du Pont Laboratory, a state-of-the-art facility for chemistry and biochemistry research.
Nathans was born October 28,1928, in Wilmington, Del. He went on to study medicine at Washington University in St. Louis, completing his residency at Presbyterian Hospital in New York, and then undertook further research in biochemistry at Rockefeller University. Moving to Johns Hopkins University, Nathans taught in the department of microbiology for 37 years, served as department chairman and then as interim president in 1995. An inspiring mentor to his numerous graduate students, as well as a gifted researcher, Dr. Nathans also took to the task of presidential fundraiser. According to an article in the Baltimore Sun, during the year Dr. Nathans served as president, the “university and hospital received a record $125.9 million from private donors.”
In addition to the Nobel Prize, Nathans also received the highest award in science in the United States--the National Medal of Science. He died November 16, 1999, in Baltimore, Md., at the age of 71. Johns Hopkins University School of Medicine named the McKusick-Nathans Institute of Genetic Medicine in his honor posthumously along with Victor McKusick. In 2005, the School of Medicine named one of its four colleges after Nathans.
joined UD in December 2007.
University of Delaware