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

Machine translation (MT) systems are ubiquitous. This ubiquity is due to a combination of increased requirement of translation in today's global marketplace, as well as an exponential development in computing energy that has made such systems viable. And under the right circumstances, MT systems can be a powerful tool. They have low-quality translations in situations where low-quality translation is better than no translation at all, or the place where a rough translation of a large document delivered within minutes or minutes is a lot more useful than a good translation delivered in three weeks' time.

Unfortunately, despite the widespread accessibility of MT, it's clear how the purpose and limitations for these systems are generally misunderstood, in addition to their capability widely overestimated. In this post, I would like to provide a brief overview of how MT systems work and therefore how they may be placed to best use. Then, I'll present some data on what Internet-based MT will be used at this time, and demonstrate that http://www.eloquia.com you will find there's chasm involving the intended and actual utilization of such systems, and that 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 with the languages in question, combining them some kind of in-memory "dictionary" to produce the resulting translation. And even, that's essentially how some earlier systems worked. But a majority of modern MT systems actually have a statistical approach that's quite "linguistically blind". Essentially, the system is trained on a corpus of example translations. The result is a statistical model that incorporates information including:

- "when the language (a, b, c) appear in succession in a sentence, there's an X% chance that the words (d, e, f) will happen in succession within the translation" (N.B. there doesn't have to be a similar number of words in each pair); - "given two successive words (a, b) in the target language, if word (a) leads to -X, it has an X% chance that word (b) can finish in -Y".

Given a tremendous body of which observations, the machine will then translate a sentence by considering various candidate translations-- made by stringing words together almost at random (in fact, via some 'naive selection' process)-- picking the statistically more than likely option.

On hearing this high-level description of methods MT works, most people are surprised that this kind of "linguistically blind" approach works at all. What's a lot more surprising is that it typically is more effective than rule-based systems. This really is partly because relying on grammatical analysis itself introduces errors in the equation (automated analysis just isn't completely accurate, and humans don't always agree on how to analyse a sentence). And training a system on "bare text" permits you to base something on much more data than would otherwise be possible: corpora of grammatically analysed texts are small, and few in number; pages of "bare text" are available in their trillions.

However, what this method means is that the quality of translations is very dependent upon how good portions of the source text are represented inside data originally employed to train the system. Should you accidentally type he'll returned or vous avez demander (rather than he'll return or vous avez demande), the device is going to be hampered by the fact that sequences like will returned are unlikely to own occurred often times inside the training corpus (or worse, may have occurred using a completely different meaning, such as they needed his will returned on the solicitor). Because the system has little thought of grammar (to work through, by way of example, that returned is really a type of return, and "the infinitive is probably after he will"), it in place has little to be on.

Similarly, you could ask the machine to translate a sentence that's perfectly grammatical and common in everyday use, but which includes features that happen to not have been common in the training corpus. MT systems are usually trained around the varieties of text which is why human translations are all around, for example technical or business documents, or transcripts of meetings of multilingual parliaments and conferences. This offers MT systems an organic bias towards certain kinds of formal or technical text. And even if everyday vocabulary remains to be taught in training corpus, the grammar each day speech (such as using tu as an alternative to usted in Spanish, or while using present tense instead of the future tense in a variety of languages) might not.

MT systems in practice

Researches and developers pc translation systems have invariably been conscious that one of the biggest dangers is public misperception with their purpose and limitations. Somers (2003)[1], observing the use of MT on the internet and in boards, comments that: "This increased visibility of MT has had a number of side effets. [...] There is certainly a desire to teach the public in regards to the substandard quality of raw MT, and, importantly, why the product quality can be so low." Observing MT used in 2009, there's sadly little evidence that users' understanding these complaints has improved.

For example, I'll present a little sample of knowledge from the Spanish-English MT service i make available with the Espanol-Ingles web site. The service functions by utilizing the user's input, applying some "cleanup" processes (for example correcting some common orthographical errors and decoding common installments of "SMS-speak"), and then trying to find translations in (a) a bank of examples through the site's Spanish-English dictionary, and (b) a MT engine. Currently, Google Translate can be used for that MT engine, although a custom engine may be used down the road. The figures I present allow me to share from an analysis of 549 Spanish-English queries presented to the device from machines in Mexico[2]-- in other words, we believe that most users are translating from other native language.

First, exactly what are people while using the MT system for? Per query, I attempted a "best guess" with the user's purpose for translating the query. In many cases, the purpose is fairly obvious; in some cases, there is certainly clearly ambiguity. With this caveat, I judge that within 88% of cases, the intended use is fairly clear-cut, and categorise these uses the following:

Looking up a single word or term: 38% Translating an elegant text: 23% Internet chat session: 18% Homework: 9% An amazing (or even alarming!) observation is that in such a large proportion of cases, users are utilizing the translator to find out just one word or term. The truth is, 30% of queries was comprised of a single word. The finding is a little surprising considering the fact that the web page involved also offers a Spanish-English dictionary, and implies that users confuse the objective of dictionaries and translators. But not represented inside raw figures, there were clearly some cases of consecutive searches where it appeared which a user was deliberately separating a sentence or phrase that could have in all probability been better translated if left together. Perhaps on account of student over-drilling on dictionary usage, we view, for instance, a question for cuarto para ("quarter to") followed immediately by a query for any number. There is clearly a requirement to teach students and users generally speaking around the distinction between the electronic dictionary as well as the machine translator[3]: in particular, that the dictionary will slowly move the user to choosing the proper translation given the context, but requires single-word or single-phrase lookups, whereas a translator generally is most effective on whole sentences and given one particular word or term, will simply report the statistically most common translation.

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

The utilization for translating formal texts is almost rivalled with the use to translate informal on-line chat sessions-- a context in which MT systems are typically not trained. The on-line chat context poses particular trouble for MT systems, since features such as non-standard spelling, deficiency of punctuation and presence of colloquialisms not present in other written contexts are normal. For chat sessions to become translated effectively may possibly have to have a dedicated system trained with a considerably better (and perhaps custom-built) corpus.