Visual Translation Cards with Contextual Definitions
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Solution Overview
Problem
Conventional language translation techniques lose contextual information when users select words from documents, leading to inappropriate translations due to the lack of context, making it difficult for users to discern the correct meaning of words with multiple meanings.
Innovation Solution
A computer-implemented technique that provides visual translation cards including semantic meanings, images, parts of speech, and candidate translations, allowing users to transition between different translations, which are compiled using a machine-trained database to maintain contextual relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional translation techniques are used to translate selected words from documents, then translation speed is improved, but translation accuracy deteriorates due to loss of contextual information
Solution Approach 1:
The system performs preliminary extraction of contextual information from the document before translation. When a user selects a word, the system automatically captures surrounding text, sentence structure, and semantic context, preparing this information in advance to guide the translation process and ensure accuracy while maintaining speed.
Solution Approach 2:
The system introduces an intermediary contextual analysis layer between the source word and target translation. This intermediary process analyzes the selected word within its document context, identifying relevant semantic relationships and usage patterns, then uses this analysis to select the most appropriate translation from multiple candidates.
2Adaptability or versatility
If multiple candidate translations are provided for a selected word, then translation completeness is improved, but user confusion increases due to difficulty in discerning correct meaning
Solution Approach 1:
The system applies local quality by providing different levels of translation information based on contextual analysis. For words with clear context, it provides a single high-confidence translation. For ambiguous words, it provides multiple candidates but prioritizes them based on contextual relevance, and includes contextual examples that illustrate appropriate usage for each candidate.
Solution Approach 2:
The system incorporates feedback mechanisms by analyzing user interactions with translation candidates. When users select or reject certain translations, the system learns from this feedback and refines its contextual analysis to better predict user preferences and provide more accurate translation recommendations in future interactions.
3Measurement precision
If contextual information is included in translation, then translation accuracy is improved, but information processing complexity increases
Solution Approach 1:
The system extracts only the most relevant contextual information needed for accurate translation, rather than processing all available document information. It identifies and extracts key elements such as surrounding sentences, grammatical structures, and semantic relationships, discarding irrelevant information to reduce processing complexity while maintaining translation accuracy.
Solution Approach 2:
The system segments the contextual analysis into distinct processing stages: initial context extraction, semantic relationship identification, translation candidate generation, and contextual example selection. This segmentation allows each stage to focus on specific tasks, improving efficiency and reducing overall processing complexity while maintaining comprehensive contextual analysis.
Data Source
AI summary
Computer-implemented techniques can include receiving a selected word in a source language, obtaining one or more parts of speech for the selected word, and for each of the one or more parts-of-speech, obtaining candidate translations of the selected word to a different target language, each candidate translation corresponding to a particular semantic meaning of the selected word. The techniques can include for each semantic meaning of the selected word: obtaining an image corresponding to the semantic meaning of the selected word, and compiling translation information including (i) the semantic meaning, (ii) a corresponding part-of-speech, (iii) the image, and (iv) at least one corresponding candidate translation. The techniques can also include outputting the translation information.