Multi-Language Entity Name Input via Probabilistic Translation Ranking
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Solution Overview
Problem
Existing input method editors (IMEs) for computing devices do not efficiently allow users to input names of entities in multiple languages, particularly failing to provide accurate translations for logogram-based and syllabogram-based languages like Chinese and Japanese, and do not offer simultaneous translations into other alphabet-based languages such as English.
Innovation Solution
A computer-implemented technique that receives a Pinyin word or a word from an alphabet-based language transliterating a non-alphabet-based language, compares it to potential translations in both the non-alphabet-based and another alphabet-based language using datastores and a language model, generates probability scores, and provides the user with a ranked list of translations for selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing input method editors are used, then users can input text in basic languages, but they cannot efficiently input names of entities in multiple languages including accurate translations for logogram-based and syllabogram-based languages
Solution Approach 1:
The input method editor is enhanced to support multiple language pairs simultaneously, including logogram-based languages (Chinese, Japanese, Korean), syllabogram-based languages (Thai, Vietnamese), and alphabet-based languages. The system provides both transliteration and translation functions in a single unified interface, allowing users to input text in one language and receive accurate translations in multiple target languages.
Solution Approach 2:
The system introduces an intermediary translation layer that processes input text through multiple linguistic models and datastores to generate accurate translations. This intermediary process includes comparing input words against datastores containing proper nouns and translations, using language models to generate probability scores, and selecting the most appropriate translations before presenting them to the user.
2Productivity
If traditional input methods are used, then the device operation is simple, but the input speed and accuracy for multi-language entity names is slow
Solution Approach 1:
The system performs preliminary actions by pre-populating datastores with proper nouns, entity names, and their translations across multiple languages before the user needs them. When a user inputs text, the system has already prepared potential translations and can quickly retrieve and present them, significantly reducing input time without adding visible complexity to the user interface.
Solution Approach 2:
The system replaces manual translation and lookup processes with automated language models and computational algorithms. Instead of requiring users to manually search for translations or switch between multiple applications, the system uses probabilistic language models to automatically generate and rank translation candidates, substituting mechanical user actions with intelligent automated processing.
3Adaptability or versatility
If existing IMEs are used, then the interface is simple, but they do not provide simultaneous translations into multiple alphabet-based languages such as English
Solution Approach 1:
The system adds a new dimension to the input interface by displaying translations in multiple target languages simultaneously within the same input field or adjacent display area. Instead of requiring users to switch between different language modes or applications, the system presents multiple language versions side-by-side or in an expanded view, allowing users to select the desired translation without navigating away from the current interface.
Data Source
AI summary
A computer-implemented technique includes receiving a first input from a user at a user device, the first input including a first word of a first alphabet-based language, which is a transliteration of a non-alphabet-based language, which is one of a logogram-based language and a syllabogram-based language. The technique then compares the first words to pluralities of potential translated words from one or more datastores associated with a second alphabet-based language and the logogram-based or syllabogram-based languages. The technique may then generate a probability score for each of the pluralities of potential translated words, the probability score indicating a likelihood of an appropriate translation. The techniques may then provide the user with some or all of the pluralities of potential translated words and the user may select an appropriate translated word to obtain a selected word, which may then be displayed via a display of the user device.