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Round-trip translation (RTT), also known as back-and-forth translation, recursive translation and bi-directional translation, is the process of translating a word, phrase or text into another language (forward translation), then translating the result back into the original language (back translation), using machine translation (MT) software.
Although Google has deployed a new system called neural machine translation for better quality translation, there are languages that still use the traditional translation method called statistical machine translation. It is a rule-based translation method that uses predictive algorithms to guess ways to translate texts in foreign languages.
It is the dominant approach today [1]: 293 [2]: 1 and can produce translations that rival human translations when translating between high-resource languages under specific conditions. [3] However, there still remain challenges, especially with languages where less high-quality data is available, [ 4 ] [ 5 ] [ 1 ] : 293 and with domain shift ...
Specific technologies in place for CLIR services include morphological analysis to handle inflection, decompounding or compound splitting to handle compound terms, and translations mechanisms to translate a query from one language to another. The first workshop on CLIR was held in Zürich during the SIGIR-96 conference. [5]
A round-trip translation is not testing one system, but two systems: the language pair of the engine for translating into the target language, and the language pair translating back from the target language. Consider the following examples of round-trip translation performed from English to Italian and Portuguese from Somers (2005):
[12] [13] These texts are sourced mainly from films, books, and governmental documents, allowing users to see idiomatic usages of translations as well as synonyms and voice output. The Reverso Context app also provides language-learning features such as flashcards based on words in example sentences. [ 14 ]
The translation then proceeds by converting sentences from the first language into sentences closer to the target language through two stages. The system may also be set up such that the second interlingua uses a more specific vocabulary that is closer, or more aligned with the target language, and this could improve the translation quality.
The following table compares the number of languages which the following machine translation programs can translate between. (Moses and Moses for Mere Mortals allow you to train translation models for any language pair, though collections of translated texts (parallel corpus) need to be provided by the user.