Abstract: A method for machine translation, comprising receiving a source string in a source language, an indication of a target language, and user identification information. The method includes using the user identification information to identify at least one memory with user-specific translation data. The method includes tokenizing the source string, using at least one processor, to produce a tokenized source string comprising any unique temporary textual elements associated with corresponding target textual elements during tokenization. The method includes obtaining a translated string from a translator, the translated string being at least a partial translation of the tokenized source string and including all of the unique temporary textual elements. The method includes generating an output string using at least one processor, the generation comprising replacing all of the unique temporary textual elements in the translated string with the associated target textual elements.

Abstract: A method for machine translation, comprising receiving a source string in a source language, an indication of a target language, and user identification information. The method includes using the user identification information to identify at least one memory with user-specific translation data. The method includes tokenizing the source string, using at least one processor, to produce a tokenized source string comprising any unique temporary textual elements associated with corresponding target textual elements during tokenization. The method includes obtaining a translated string from a translator, the translated string being at least a partial translation of the tokenized source string and including all of the unique temporary textual elements. The method includes generating an output string using at least one processor, the generation comprising replacing all of the unique temporary textual elements in the translated string with the associated target textual elements.

Abstract: A method for machine translation, comprising receiving a source string in a source language, an indication of a target language, and user identification information. The method includes using the user identification information to identify at least one memory with user-specific translation data. The method includes tokenizing the source string, using at least one processor, to produce a tokenized source string comprising any unique temporary textual elements associated with corresponding target textual elements during tokenization. The method includes obtaining a translated string from a translator, the translated string being at least a partial translation of the tokenized source string and including all of the unique temporary textual elements. The method includes generating an output string using at least one processor, the generation comprising replacing all of the unique temporary textual elements in the translated string with the associated target textual elements.

Abstract: A method for machine translation, comprising receiving a source string in a source language, an indication of a target language, and user identification information. The method includes using the user identification information to identify at least one memory with user-specific translation data. The method includes tokenizing the source string, using at least one processor, to produce a tokenized source string comprising any unique temporary textual elements associated with corresponding target textual elements during tokenization. The method includes obtaining a translated string from a translator, the translated string being at least a partial translation of the tokenized source string and including all of the unique temporary textual elements. The method includes generating an output string using at least one processor, the generation comprising replacing all of the unique temporary textual elements in the translated string with the associated target textual elements.

Abstract: Systems and methods for multi-engine machine translations are disclosed. Exemplary methods and systems involve normalizing and/or tokenizing a source string using user-specific translation data. The user-specific translation data may include glossary data, translation memory data, and rule data for use in customizing translations and sequestering sensitive data during the translation process. The disclosed methods and systems also involve using one or more machine translation engines to obtain a translation of the normalized and/or tokenized source string.

Abstract: Systems and methods for multi-engine machine translations are disclosed. Exemplary methods and systems involve normalizing and/or tokenizing a source string using user-specific translation data. The user-specific translation data may include glossary data, translation memory data, and rule data for use in customizing translations and sequestering sensitive data during the translation process. The disclosed methods and systems also involve using one or more machine translation engines to obtain a translation of the normalized and/or tokenized source string.