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Machine Translation - How it operates, What Users Expect, and What They Get

Machine translation (MT) systems are ubiquitous. This ubiquity is because of a variety of increased requirement of translation in our global marketplace, as well as an exponential increase in computing energy that has created such systems viable. And beneath the right circumstances, MT systems are a powerful tool. They feature low-quality translations in situations where low-quality translation is preferable to no translation whatsoever, or in which a rough translation of a giant document delivered within minutes or minutes is a bit more useful compared to a good translation delivered in three weeks' time.

Unfortunately, in spite of the widespread accessibility of MT, it can be clear the purpose and limitations of such systems are generally misunderstood, in addition to their capability widely overestimated. In the following paragraphs, I would like to give a brief introduction to how MT systems work and thus how you can be placed to best use. Then, I'll present some data how Internet-based MT has been used at this time, and reveal that http://www.eloquia.com you will find there's chasm between your intended and actual usage of such systems, which users still need educating on the way to use MT systems effectively.

How machine translation works

It's likely you have expected which a computer translation program would use grammatical rules of the languages involved, combining these with some kind of in-memory "dictionary" to create the resulting translation. And indeed, that's essentially how some earlier systems worked. But a majority of modern MT systems actually require a statistical approach which is quite "linguistically blind". Essentially, it is trained with a corpus of example translations. It makes sense a statistical model that incorporates information such as:

- "when the text (a, b, c) happen in succession in the sentence, it comes with an X% chance that the words (d, e, f) will occur in succession in the translation" (N.B. there must not be the identical quantity of words in each pair); - "given two successive words (a, b) inside the target language, if word (a) ends in -X, there is an X% chance that word (b) will end in -Y".

Given a massive body for these observations, the device will then translate a sentence by considering various candidate translations-- made by stringing words together almost randomly (in fact, via some 'naive selection' process)-- deciding on the statistically almost certainly option.

On hearing this high-level description of how MT works, many people are surprised that such a "linguistically blind" approach works whatsoever. What's much more surprising is that it typically works more effectively than rule-based systems. This really is partly because relying on grammatical analysis itself introduces errors into the equation (automated analysis just isn't completely accurate, and humans don't always concur with the best way to analyse a sentence). And training something on "bare text" allows you to base a process on a great deal more data than would otherwise be possible: corpora of grammatically analysed texts are small, few and far between; pages of "bare text" can be found in their trillions.

However, what this method includes is the quality of translations is extremely influenced by how well aspects of the cause text are represented inside the data originally utilized to train the machine. If you accidentally type he will returned or vous avez demander (as an alternative to he will return or vous avez demande), the device is going to be hampered by the fact that sequences for example will returned are unlikely to possess occurred many times within the training corpus (or worse, may have occurred which has a completely different meaning, such as they needed his will returned to the solicitor). And since the system has little thought of grammar (to sort out, as an example, that returned can be a kind of return, and "the infinitive is probable after he will"), it in place has little to be.

Similarly, you might ask it to translate a sentence that is certainly perfectly grammatical and common in everyday use, but which includes features which happen not have been common inside the training corpus. MT systems are usually trained for the types of text in which human translations are plentiful, like technical or business documents, or transcripts of meetings of multilingual parliaments and conferences. This offers MT systems an all-natural bias towards some kinds of formal or technical text. And even if everyday vocabulary is still taught in training corpus, the grammar of everyday speech (like using tu instead of usted in Spanish, or while using present tense rather than the future tense in several languages) may not.

MT systems in reality

Researches and developers of computer translation systems have always been conscious one of the greatest dangers is public misperception of the purpose and limitations. Somers (2003)[1], observing using MT on the web and in forums, comments that: "This increased visibility of MT has already established many side effets. [...] There is certainly a desire to teach the general public regarding the inferior of raw MT, and, importantly, why the standard is so low." Observing MT being used in '09, there's sadly little evidence that users' understanding these issues has improved.

For instance, I'll present a small sample of data coming from a Spanish-English MT service which i provide on the Espanol-Ingles web page. The service functions utilizing the user's input, applying some "cleanup" processes (such as correcting some common orthographical errors and decoding common cases of "SMS-speak"), after which trying to find translations in (a) a bank of examples in the site's Spanish-English dictionary, and (b) a MT engine. Currently, Google Translate is employed to the MT engine, although a custom engine works extremely well later on. The figures I present here are from an analysis of 549 Spanish-English queries shown to the system from machines in Mexico[2]-- to put it differently, we believe that most users are translating from their native language.

First, precisely what are people while using MT system for? For each and every query, I could a "best guess" at the user's purpose for translating the query. Most of the time, the reason is pretty obvious; in a few cases, there is clearly ambiguity. With that caveat, I judge that inside 88% of cases, the intended use is fairly clear-cut, and categorise these uses the following:

Searching for a single word or term: 38% Translating a proper text: 23% Internet chat session: 18% Homework: 9% An unexpected (or else alarming!) observation is always that in that large proportion of cases, users are using the translator to find out one particular word or term. In fact, 30% of queries contained just one word. The finding is a bit surprising considering that your website involved also has a Spanish-English dictionary, and shows that users confuse the goal of dictionaries and translators. However, not represented inside the raw figures, there are clearly certain cases of consecutive searches where it appeared that the user was deliberately separating a sentence or phrase that would have likely been better translated if left together. Perhaps as a consequence of student over-drilling on dictionary usage, we see, for instance, a question for cuarto para ("quarter to") followed immediately by a query for any number. There's clearly a need to educate students and users generally speaking about the difference between the electronic dictionary and the machine translator[3]: specifically, that a dictionary will advice the user to picking the proper translation due to the context, but requires single-word or single-phrase lookups, whereas a translator generally is most effective on whole sentences and given just one word or term, will simply report the statistically most frequent translation.

I estimate that in under a quarter of cases, users are utilizing the MT system due to the "trained-for" reason for translating or gisting an elegant text (and are entering a full sentence, at least partial sentence rather than a remote noun phrase). Obviously, it's impossible to know whether any of these translations were then meant for publication without further proof, which definitely isn't function of the machine.

The utilization for translating formal texts is now almost rivalled through the use to translate informal on-line chat sessions-- a context that MT systems are normally not trained. The on-line chat context poses particular problems for MT systems, since features including non-standard spelling, not enough punctuation and presence of colloquialisms not found in other written contexts are routine. For chat sessions being translated effectively would probably demand a dedicated system trained on the more suitable (and perhaps custom-built) corpus.