The short answer is that no one wants to watch a starship crew spend years compiling an alien dictionary.

簡而言之，沒有人會想要看一群太空船員們耗上多年光陰編纂一部外星辭典。

But to keep things consistent, the creators of Star Trek and other science-fiction worlds have introduced the concept of a universal translator, a portable device that can instantly translate between any languages.

但為了保持不同語言使用者間都具有翻譯的一致性，《星際迷航記》和許多科幻作品的創作者皆引進了一個可以迅速地在任何語言之間進行翻譯的攜帶式裝置，也就是「通用翻譯器」的概念。

So is a universal translator possible in real life?

既然如此，通用翻譯器在現實生活中是可行的嗎？

We already have many programs that claim to do just that, taking a word, sentence, or entire book in one language and translating it into almost any other, whether it's modern English or Ancient Sanskrit.

其實現在已經有不少的運算程式宣稱能做到這點。例如從某個語言中取一個字、擷一句話或帶入一整本書， 將其翻譯成幾乎任何一種語言，無論是現時的英文或古老的梵語皆可。

And if translation were just a matter of looking up words in a dictionary, these programs would run circles around humans.

如果翻譯就只是單純翻開字典查閱單字而已的話，那麼這些程式老早就超越人類了。

The reality, however, is a bit more complicated.

然而，實際狀況卻稍微複雜一些。

A rule-based translation program uses a lexical database, which includes all the words you'd find in a dictionary and all grammatical forms they can take, and a set of rules to recognize the basic linguistic elements in the input language.

基於運算規則的翻譯程式利用的是辭彙資料庫，在裡面包含了所有能在字典裡找到的字、它們能利用的文法形式，以及一套用來辨識輸入語言中基礎語法成分的規則。

For a seemingly simple sentence like, "The children eat the muffins," the program first parses its syntax, or grammatical structure, by identifying the children as the subject, and the rest of the sentence as the predicate consisting of a verb "eat," and a direct object "the muffins."

舉一個看似簡單的例子，「The children eat the muffins (孩子們吃了些杯子蛋糕)」。程式會先剖析這句的「句法」規則，或是文法結構。 於是程式把「children (孩子們)」認定為主詞， 句子的剩餘部分認定為包含「eat (吃)」這一個動詞， 以及一個直接受詞「the muffins (杯子蛋糕)」的「謂語」。

It then needs to recognize English morphology, or how the language can be broken down into its smallest meaningful units, such as the word muffin and the suffix "s," used to indicate plural.

它接著需要辨認英文的詞法結構，也就是如何將語言拆解成最小的意義單位。 比方說「muffin (杯子蛋糕)」這個單詞以及後綴用來表示複數的「s」。

Finally, it needs to understand the semantics, what the different parts of the sentence actually mean.

最後，它需要了解句子的「語義」，也就是句子中不同的部分各自帶有什麼樣的意義。

To translate this sentence properly, the program would refer to a different set of vocabulary and rules for each element of the target language.

為了得體地翻譯出這個句子，程式會參照一套不同的詞彙表和規則， 來比照對應標的語言中的每項語素。

But this is where it gets tricky.

但這裡就是開始變得弔詭難搞的地方。

The syntax of some languages allows words to be arranged in any order, while in others, doing so could make the muffin eat the child.

有些語言的文法結構就算字詞以不同的順序排列也不會影響語意。但在其他語言中，這麼做可能會讓語意變成「the muffin eat the child (杯子蛋糕吃了孩子)」。

Morphology can also pose a problem.

構詞學也會構成一大問題。

Slovene distinguishes between two children and three or more using a dual suffix absent in many other languages, while Russian's lack of definite articles might leave you wondering whether the children are eating some particular muffins, or just eat muffins in general.

斯洛維尼亞語辨別兩三個小孩或更多小孩的方式，是使用許多其他語言少見的雙重後綴語尾。相較之下，俄羅斯文則欠缺了定冠詞，可能會使人對到底這些小孩是在吃一些特定的杯子蛋糕， 還是只是吃了普通而沒有指定的杯子蛋糕而感到困惑。

Finally, even when the semantics are technically correct, the program might miss their finer points, such as whether the children "mangiano" the muffins, or "divorano" them.

最後，即使技術層面上程式正確地掌握了語義，它們仍可能忽略掉一些較為精細的問題。 例如到底小孩們只是「mangiano (義大利文第三人稱複數的吃)」了杯子蛋糕， 還是「divorano (義大利文第三人稱複數的狼吞虎嚥)」了杯子蛋糕。

Another method is statistical machine translation, which analyzes a database of books, articles, and documents that have already been translated by humans.

除了上述方法之外的另一個方式是統計機器翻譯，藉由分析一個已經被人類翻譯過的充滿書籍、文章和文件的資料庫來進行翻譯。

By finding matches between source and translated text that are unlikely to occur by chance, the program can identify corresponding phrases and patterns, and use them for future translations.

藉由搜尋來源語言和標的語言之間鮮少單純因偶然而相互匹配的用法， 程式便能藉此辨別對應的片語和用語模式， 並將在之後進行翻譯時套用。

However, the quality of this type of translation depends on the size of the initial database and the availability of samples for certain languages or styles of writing.

然而這種形式翻譯的品質取決於原始資料庫的大小， 以及針對特定語言用法或是寫作風格例子的可用性。　

The difficulty that computers have with the exceptions, irregularities and shades of meaning that seem to come instinctively to humans has led some researchers to believe that our understanding of language is a unique product of our biological brain structure.

電腦會遭遇的難題，例如例外用法、不規則用法和某些字詞間意義的細微差別，人類卻有辦法本能地接受它們， 讓研究人員相信我們對於人類語言的理解， 是生理大腦結構的獨特結果。

In fact, one of the most famous fictional universal translators, the Babel fish from "The Hitchhiker's Guide to the Galaxy", is not a machine at all but a small creature that translates the brain waves and nerve signals of sentient species through a form of telepathy.

事實上，科幻作品裡各個通用翻譯機中最名聞遐邇的一個便是《銀河便車指南》裡的巴別魚。 「牠」壓根不是一部機器，而是一隻微小的生物， 能藉由心電感應來翻譯有意識物種的腦波及神經信號。

For now, learning a language the old fashioned way will still give you better results than any currently available computer program.

就目前來說，比起借助眼下任一現成的電腦程式，以老一套的方法學習語言的成效更佳。

But this is no easy task, and the sheer number of languages in the world, as well as the increasing interaction between the people who speak them, will only continue to spur greater advances in automatic translation.

但老派學習法費時費力，加上世界上數不盡的語言數量，以及不同語言使用者之間的交流與日俱增， 種種形勢使然下，只會持續刺激自動翻譯技術更急遽地步。

Perhaps by the time we encounter intergalactic life forms, we'll be able to communicate with them through a tiny gizmo, or we might have to start compiling that dictionary, after all.

或許到了我們真的遇到外星生物的時候，我們已經能透過一個迷你的裝置與它們溝通了。又或許儘管說了這麼多，我們最終還是得從頭編纂一部宇宙辭典。