Abstract: A software input string is received and tokenized into a sequence of tokens. The sequence of tokens is applied to a trained sequence-dependent lock/unlock classifier so that each of the tokens is classified as a token that should be locked, or remain unlocked, for subsequent translation. The software input string is converted to a converted string, in which the locked tokens are identified and the converted string is submitted for machine translation. A machine translation result is received and converted so that the locked tokens are replaced in the machine translation result, to obtain a translated software string.

Abstract: A software input string is received and tokenized into a sequence of tokens. The sequence of tokens is applied to a trained sequence-dependent lock/unlock classifier so that each of the tokens is classified as a token that should be locked, or remain unlocked, for subsequent translation. The software input string is converted to a converted string, in which the locked tokens are identified and the converted string is submitted for machine translation. A machine translation result is received and converted so that the locked tokens are replaced in the machine translation result, to obtain a translated software string.

Abstract: In one embodiment, a translation system may use a translation bug prediction model to more efficiently identify translation errors in a user interface text string. The translation system may apply a translation bug prediction model to a translation resource to identify a potential error source. The translation system may associate an attention flag with the translation resource when identified as the potential error source. The translation system may execute an automatic translation of the translation resource to create a translation target.

Abstract: In various embodiments, methods and systems for rebalancing database shards are provided. Candidate entities in the database shards are identified for rebalancing. The candidate entities have corresponding existing locations in the database shards. New locations are determined for the candidate entities in the database shards. A shard map is created that identifies the new locations in the database shards for the identified candidate entities. When data for the identified candidate entities is received, the data for the identified candidate entities is written to the locations in the database shards identified in the shard map. Existing data for the identified candidate entities is maintained at their corresponding existing locations in the database shards.

Abstract: Embodiments relate to automatically providing textual context for source strings in a source language that are to be translated by a human translator to target strings in a target language. The source strings are compared against a dictionary of reference strings in the source language. For each source string, one or more of the reference strings that are most relevant, similar, etc., are selected. When a human translator is to translate the source strings, the selected reference strings are presented; each source string has one or more similar/related strings displayable in association therewith. For a given source string, the human translator can use the associated reference strings as a form of context to help estimate the intended meaning of the given source string when translating the given source string to a target string in the target language.

Abstract: Embodiments relate to automatically providing textual context for source strings in a source language that are to be translated by a human translator to target strings in a target language. The source strings are compared against a dictionary of reference strings in the source language. For each source string, one or more of the reference strings that are most relevant, similar, etc., are selected. When a human translator is to translate the source strings, the selected reference strings are presented; each source string has one or more similar/related strings displayable in association therewith. For a given source string, the human translator can use the associated reference strings as a form of context to help estimate the intended meaning of the given source string when translating the given source string to a target string in the target language.

Abstract: In one embodiment, a translation system may use a translation bug prediction model to more efficiently identify translation errors in a user interface text string. The translation system may apply a translation bug prediction model to a translation resource to identify a potential error source. The translation system may associate an attention flag with the translation resource when identified as the potential error source. The translation system may execute an automatic translation of the translation resource to create a translation target.