Statistical machine translation

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.

Basis

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

The problem of modeling the probability distribution $p(e|f)$ has been approached in a number of ways. One intuitive approach is to apply Bayes Theorem, that is $p(e|f)\propto p(f|e)p(e)$, where the translation model $p(f|e)$ is the probability that the source string is the translation of the target string, and the language model $p(e)$ is the probability of seeing that target language string string. This decomposition is attractive as it splits the problem into two subproblems. Finding the best translation ${\tilde {e}}$ is done by picking up the one that gives the highest probability:

${\tilde {e}}=arg\max _{e\in e^{*}}p(e|f)=arg\max _{e\in e^{*}}p(f|e)p(e)$.

For a rigorous implementation of this one would have to perform an exhaustive search by going through all strings $e^{*}$ in the native language. Performing the search efficiently is the work of a machine translation decoder that uses the foreign string, heuristics and other methods to limit the search space and at the same time keeping acceptable quality. This trade-off between quality and time usage can also be found in speech recognition.

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.

The statistical translation models were initially word based (Models 1-5 from IBM Hidden Markov Model from Stephan Vogel and Model 6 from Franz-Joseph Och), but significant advances were made with the introduction of phrase based models. Recent work has incorporated syntax or quasi-syntactic structures.

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

Word-based translation

In word-based translation, the fundamental unit of translation is a word in some natural language. Typically, the number of words in translated sentences are different, because of compound words, morphology and idioms. The ratio of the lengths of sequences of translated words is called fertility, which tells how many foreign words each native word produces. Necessarily it is assumed by information theory that each covers the same concept. In practice this is not really true. For example, the English word corner can be translated in Spanish by either rincón or esquina, depending on whether it is to mean its internal or external angle.

Simple word-based translation can't translate between languages with different fertility. Word-based translation systems can relatively simply be made to cope with high fertility, but they could map a single word to multiple words, but not the other way about. For example, if we were translating from French to English, each word in English could produce any number of French words— sometimes none at all. But there's no way to group two English words producing a single French word.

An example of a word-based translation system is the freely available GIZA++ package (GPLed), which includes the training program for IBM models and HMM model and Model 6.

The word-based translation is not widely used today; phrase-based systems are more common. Most phrase-based system are still using GIZA++ to align the corpus. The alignments are used to extract phrases or deduce syntax rules. And matching words in bi-text is still a problem actively discussed in the community. Because of the predominance of GIZA++, there are now several distributed implementations of it online.

Phrase-based translation

In phrase-based translation, the aim is to reduce the restrictions of word-based translation by translating whole sequences of words, where the lengths may differ. The sequences of words are called blocks or phrases, but typically are not linguistic phrases but phrases found using statistical methods from corpora. It has been shown that restricting the phrases to linguistic phrases (syntactically motivated groups of words, see syntactic categories) decreases the quality of translation

Challenges with statistical machine translation

Problems that statistical machine translation have to deal with include

Idioms

Depending on the corpora used, idioms may not translate "idiomatically". For example, using Canadian Hansard as the bilingual corpus, "hear" may almost invariably be translated to "Bravo!" since in Parliament "Hear, Hear!" becomes "Bravo!".

Different word orders

Word order in languages differ. Some classification can be done by naming the typical order of subject (S), verb (V) and object (O) in a sentence and one can talk, for instance, of SVO or VSO languages. There are also additional differences in word orders, for instance, where modifiers for nouns are located, or where the same words are used as a question or a statement.

In speech recognition, the speech signal and the corresponding textual representation can be mapped to each other in blocks in order. This is not always the case with the same text in two languages. For SMT, the machine translator can only manage small sequences of words, and word order has to be thought of by the program designer. Attempts at solutions have included re-ordering models, where a distribution of location changes for each item of translation is guessed from aligned bi-text. Different location changes can be ranked with the help of the language model and the best can be selected.

Out of vocabulary (OOV) words

SMT systems store different word forms as separate symbols without any relation to each other and word forms or phrases that were not in the training data cannot be translated. This might be because of the lack of training data, changes in the human domain where the system is used, or differences in morphology.

See also

• AppTek
• Asia Online
• Example-based machine translation
• Google Translate
• Language Weaver
• Machine translation

External links

• Statistical Machine Translation — includes introduction to research, conference, corpus and software listings.
• Moses: a state-of-the-art open source SMT system
• Annotated list of statistical natural language processing resources — Includes links to freely available statistical machine translation software.
• GIZA++: Word Alignment Tool
• MGIZA++/PGIZA++ Parallel Implementations of GIZA++
• Cunei - an open source platform for data-driven machine translation that combines the approaches of SMT and EBMT