3. Statistics-based mt systems.

Statistical machine translation (SMT) is a machine translation paradigm where translations are generated on the basis of statistical models whose parameters are derived from the analysis of bilingual text corpora. The statistical approach contrasts with the rule-based approaches to machine translation as well as with example-based machine translation.

The first ideas of statistical machine translation were introduced by Warren Weaver in 1949, including the ideas of applying Claude Shannon's information theory. Statistical machine translation was re-introduced in 1991 by researchers at IBM's Thomas J. Watson Research Center and has contributed to the significant resurgence in interest in machine translation in recent years. Nowadays it is by far the most widely-studied machine translation method.

Benefits

The most frequently cited benefits of statistical machine translation over traditional paradigms are:

Better use of resources

There is a great deal of natural language in machine-readable format.

Generally, SMT systems are not tailored to any specific pair of languages.

Rule-based translation systems require the manual development of linguistic rules, which can be costly, and which often do not generalize to other languages.

More natural translations

Rule-based translation systems are likely to result in Literal translation. While it appears that SMT should avoid this problem and result in natural translations, this is negated by the fact that using statistical matching to translate rather than a dictionary/grammar rules approach can often result in text that include apparently nonsensical and obvious errors.

Basis

The idea behind statistical machine translation comes from information theory. A document is translated according to the probability distribution that a string in the target language (for example, English) is the translation of a string in the source language (for example, French).

As the translation systems are not able to store all native strings and their translations, a document is typically translated sentence by sentence, but even this is not enough. Language models are typically approximated by smoothed n-gram models, and similar approaches have been applied to translation models, but there is additional complexity due to different sentence lengths and word orders in the languages.

4. Google Translator Toolkit.

Google Translator Toolkit is a web application designed to allow translators to edit the translations that Google Translate automatically generates. With the Google Translator Toolkit, translators can organize their work and use shared translations, glossaries and translation memories. They can upload and translate Microsoft Word documents, OpenOffice, RTF, HTML, text, and Wikipedia articles. Google Translator Toolkit is supported by Google Translate, a web-based translation service. Google Translator Toolkit can be configured to automatically pre-translate uploaded documents using Google Translate.

Google Translator Toolkit was released by Google Inc. on June 8, 2009. Google claims that Google Translator Toolkit is part of their "effort to make information universally accessible through translation" and "helps translators translate better and more quickly through one shared, innovative translation technology." Originally the Google Translator Toolkit was meant to attract collaboratively-minded people, the kind who translate Wikipedia entries or material for NGOs. However, nowadays it is more and more widely used in also commercial translation projects."The significance of the Google Translator Toolkit is its position as a fully online software-as-a-service (SaaS) that mainstreams some backend enterprise features and hitherto fringe innovations, presaging a radical change in how and by whom translation is performed."

Workflow

The workflow of Google Translator Toolkit can be described as follows. First, users upload a file from their desktop or enter a URL of a web page or Wikipedia article that they want to translate. Google Translator Toolkit automatically 'pretranslates' the document. It divides the document into segments, usually sentences, headers, or bullets. Next, it searches all available translation databases for previous human translations of each segment.Users can then work on reviewing and improving the automatic translation. They can also share their translations with their friends by clicking the "Share" button and inviting them to help edit or view their translation. When they are finished, they can download the translation to their desktop. For Wikipedia articles, they can easily publish back to the source pages.

How is this different from Google Translate? Google Translate provides ‘automatic translations’ produced purely by technology, without intervention from human translators. In contrast, Google Translator Toolkit allows human translators to work faster and more accurately, aided by technologies like Google Translate.

Here's what you can do with Google Translator Toolkit:

• Upload Word documents, OpenOffice, RTF, HTML, text, Wikipedia articles and knols.

• Use previous human translations and machine translation to 'pretranslate' your uploaded documents.

• Use our simple WYSIWYG editor to improve the pretranslation.

• Invite others (by email) to edit or view your translations.

• Edit documents online with whomever you choose.

• Download documents to your desktop in their native formats --- Word, OpenOffice, RTF or HTML.

• Publish your Wikipedia and knol translations back to Wikipedia or Knol.