1. Apples satisfy me
2. I love apples

here 2 is more fluent, because that’s what people usually say in English… a related measure is grammaticality (”I loves apples” would score lower than 2. on this measure).

Google Translate is great with fluency, mainly because it really is based on what people say in a certain language. They have a lot of data for eg. English and Spanish, so they can easily check if something sounds fluent.

What they score lower on, however, is accuracy. To give an example, entering the Swedish phrase “Själland kan ses her”, the Danish translation becomes “Zealand kan ses her”, when it should be “Sjælland kan ses her” — ie. it changed the name. This makes Google Translate unreliable for getting hold of factual information, since key words and phrases may be distorted (although they may be very “readable”, you’re reading something other than a translation).

Besides, traditionally, RBMT has been quite the slow-poke compared to SMT; if you want to make an argument for RBMT, that’s not the place to do it. What RBMT _is_ good at though, is producing translations that are true in meaning to the original text. You can easily make an SMT-system such as Google’s produce a translations that looks quite fluent, but has completely twisted the meaning (esp. proper names, like translating “Praha” (Prague) into “Dublin”). Also, RBMT has the advantage of being easy to change if someone does notice a problem, whereas changing an SMT system involves retraining on an improved corpus (this might be helped by using a TMX instead though).

I hear that Google, which uses statistical machine translation, is 4 times slower than rule-based machine translation. Also, Google scanned the UN corpus of translated docs going back to 1945, so at least for 6 official UN languages, its good for translating political science–that’s whats in the memory…so Transclick’s subject domain specific dictionaries solves that problem by having translation memory that is matching the connotations in a context-sensitive way, producing better linguistic accuracy. Check it out.

I like their promptness, quality is good, and you can’t beat the cost for human translation!

In some cases, machine translation takes jobs away from human translators. If a machine can translate with sufficient accuracy, why must a company spend money on an expensive human translator?

In many cases, machine translation does not take jobs away from human translators. Machine translation creates new markets. Content that was previously too expensive to translate by humans can now be translated by machine.

@María Florencia Russo: And what I think is even worse, some people truly believe human translation can or wil be able to be replaced by such a thing as an impersonal, insensitive, and intuitionless machine translator.

The belief of people is not important. The reality is that “today, more words are translated per year using MT [machine translation] than are translated by human translators, and the demand continues to grow” (Localization Industry Standards Association, http://www.lisa.org/Best-Practic.467.0.html).

@Maria Florencia: It is worth mentioning that the level of accuracy oftern has to do with the complexity of the target language.

The accuracy of translation depends also on the quality of the source text.

@techandlife: I’ve come across a number of blog posts mentioning online translation services but this is the first I’ve seen that looks at the quality of the translation. That’s most helpful.

For another evaluation of machine translation, see http://www.international-english.co.uk/mt-evaluation.html.

It translates your instant messages in real-time,in the language of your chat buddy.