Pinyin Input Mechanism Ranking Candidates via Language Model
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Solution Overview
Problem
Conventional Pinyin Input Methods (PIMs) face low conversion precision due to numerous Chinese character candidates for a single Pinyin syllable, requiring users to expend more effort to select the desired output text from lengthy lists.
Innovation Solution
A computer-implemented language input mechanism that converts phonetic Pinyin text into written Chinese text, utilizing a Language Model, Lexicon, and History Cache to rank candidate output words and sentences based on probability scores, reducing user interactions by learning user preferences and context.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional Pinyin Input Methods display a list of candidate characters or words for Pinyin input, then the user can select from multiple options, but the displayed list does not order the candidates so that the desired candidate is first or high on the list, resulting in low conversion precision and requiring more user effort to locate and select the desired output text
Solution Approach 1:
The system employs a Language Model that continuously learns from user selections and corrections, adjusting the ranking of candidate words based on feedback. When users select or correct candidate words, this information is fed back into the model to improve future rankings, thereby increasing conversion precision over time while reducing user effort.
Solution Approach 2:
The system dynamically changes the ranking parameters of candidate words based on contextual analysis. By evaluating the probability scores generated by the Language Model considering contextual information, the system reorders candidates to place the most likely desired word at the top, improving both conversion precision and ease of selection.
2Adaptability or versatility
If there are many Chinese character candidates for a single Pinyin syllable and many word candidates for a Pinyin syllable sequence, then the system provides comprehensive options, but the displayed list becomes lengthy and requires more user effort to select the desired output text
Solution Approach 1:
The system extracts only the most relevant candidate words from the comprehensive list based on probability scores generated by the Language Model. By filtering and ranking candidates according to their likelihood of being the desired output, the system presents a manageable subset of the most probable options, reducing list length while maintaining comprehensive coverage of valid alternatives.
Solution Approach 2:
The Language Model performs preliminary ranking and filtering of candidate words before display, using contextual analysis to pre-order candidates by probability. This preliminary action ensures that the desired word is already positioned at the top of the list when presented to the user, eliminating the need for users to search through lengthy lists while preserving all valid candidate options.
3Measurement precision
If the system displays ranked candidate output words with probability scores, then conversion precision is improved, but the system requires access to Language Model, Lexicon, and History Cache which increases system complexity
Solution Approach 1:
The Language Model serves multiple functions simultaneously: it ranks candidate words by probability, learns from user feedback, maintains historical usage patterns, and adapts to individual user preferences. By consolidating these functions into a single multi-functional component, the system achieves high conversion precision without proportionally increasing complexity, as one component performs what would otherwise require multiple separate systems.
Data Source
AI summary
Method and apparatus for converting language text input into a different language text output. One embodiment may be configured to convert Pinyin into written Chinese language text. Embodiments may provide a language input mechanism that accepts phonetic language input text and generates written language output text, and that also accepts modifications to existing input text and automatically corrects the corresponding output text. Embodiments may display one or more alternative output text words and/or sentences as candidates for the transliteration of the input text. The candidate output words may be ranked according to calculated probabilities, with a most probable candidate appearing first. The user may select from the candidate output text to build an output sentence that represents the correct or preferred transliteration of the input text. A lexicon, language model and history cache of previously selected output text words may be referenced in determining the probabilities of candidate output words.