Sentence examples for translation algorithm from inspiring English sources

In this paper a generic translation algorithm is proposed to translate a restricted natural language query (RNLQ) to a standard query language like SQL (Structured Query Language).

Initially the positions of words within each cloud are ordered at random and the translation algorithm begins training on a dataset of translated sentences.

In cases where samples displayed drift during time lapses, these were corrected using the translation algorithm in ZEN (Blue Edition).

The translation algorithm searches for the path that leads to the right sequence of words, and plays the most likely phrase on the laptop's speakers within seconds.

This vector can then be plugged into Google's existing translation algorithm to produce a caption in English, or indeed in any other language.

One could generate suboptimal SQL queries using a fairly naive translation algorithm, and then attempt to optimize the resulting SQL; or one could use a more intelligent translation algorithm with the hopes of generating efficient SQL directly.

Google, of course, also automatically captions Youtube videos for a number of languages and will happily use its machine translation algorithms to translate them into other languages, too.

The published XML-to-SQL query translation algorithms are suboptimal in that they often translate simple path expressions into complex SQL queries even when much simpler equivalent SQL queries exist.

But in recent years, Google has transformed this process by developing machine translation algorithms that change the nature of cross cultural communications through Google Translate.

It is these that cause the problems for machine translation algorithms.

One of the translation algorithms, which have been implemented in the FDAF tool support, and its proof are presented.