Social Network Sentence Syntax Translation via Context Tokens
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Solution Overview
Problem
Current social networking systems face challenges in accurately translating sentences across languages due to lack of information about actors, edges, and targets, leading to awkward or inaccurate translations, especially when gender, age, or relative age differences are involved.
Innovation Solution
An internationalization system with a developer interface allows customization of sentence syntax for translation, using tokens for actors, edges, targets, and viewers to define language-specific translations, including gender, possessive/nominative targets, and number plural types, enabling tailored translations based on viewer demographics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If machine generated translations are used for social graph expressions, then translation speed is improved, but translation accuracy deteriorates due to lack of context about actors, edges, and targets
Solution Approach 1:
The system performs preliminary actions by collecting and storing contextual information about actors, edges, targets, and viewers before the translation process. This includes gathering demographic data, relationship information, and language preferences in advance, so that when translation is needed, accurate context is already available to generate culturally appropriate translations rather than relying on generic machine translation
Solution Approach 2:
The system introduces an intermediary translation layer that sits between the social graph data and the final translated output. This intermediary component enriches the translation process by injecting contextual information about actors, edges, and targets, and by selecting appropriate language variations based on viewer demographics, thereby improving accuracy without sacrificing the efficiency of automated translation
2Ease of operation
If default sentence structure translation is used, then ease of operation is improved, but cultural relevance deteriorates due to ignoring gender, age, and other demographic factors
Solution Approach 1:
The system applies local quality by tailoring translations to specific local contexts rather than using uniform translations. It analyzes viewer demographics (gender, age, location, language preferences) and selects or generates translations that are culturally appropriate for each specific viewer context. For example, it may choose different verb forms, pronouns, or phrasing based on the gender or age of the viewer, making the translation culturally relevant while maintaining operational simplicity through automated context-aware selection
Data Source
AI summary
Sentence internationalization methods and systems are disclosed. The method may include: providing a developer interface to define an internationalized sentence syntax for an application on a social networking system, the internationalized sentence syntax for translating a natural language expression of a social graph edge of the social networking system; providing a sentence option on the developer interface to define grammar of the internationalized sentence syntax; generating a token structure including a language token to assist translation of the internationalized sentence syntax into a preferred language indicated by the language token, the token structure customizable via the developer interface to configure translation options; associating a social graph attribute with a first token of the token structure; and storing the sentence option and the token structure with the internationalized sentence syntax to facilitate run-time translation of the internationalized sentence syntax into the natural language expression in the preferred language.