Second Language Writing Advisor Using Corruption Model
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Solution Overview
Problem
Current automatic text correction programs are ineffective in identifying awkward or inappropriate word choices in second language writing, which can lead to embarrassing errors even for native speakers, as they primarily focus on spelling and grammar errors rather than contextual appropriateness.
Innovation Solution
A system that uses a language model and a corruption model to suggest alternative words for selected text, based on frequency of occurrence in a second language corpus and translation probabilities, providing authors with a menu of improved word choices to enhance contextual appropriateness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If spelling and grammar checking techniques are used, then spelling and grammar errors are identified, but awkward word choices and contextual appropriateness errors are not detected
Solution Approach 1:
The language advisor program integrates multiple functions into a single system: it performs spelling checking, grammar checking, and contextual word choice evaluation. By making the system universal, it can detect not only traditional errors but also awkward word choices and contextual appropriateness issues, resolving the contradiction between error detection accuracy and word choice evaluation capability
Solution Approach 2:
The system introduces a corruption model as an intermediary component that bridges the gap between traditional spelling/grammar checkers and contextual word choice evaluation. This mediator analyzes the probability of words being used correctly in context by comparing against a language model, enabling the detection of awkward word choices that traditional checkers would miss
2Adaptability or versatility
If a language model and corruption model are implemented to suggest alternative words, then contextual appropriateness is improved, but system complexity increases
Solution Approach 1:
The system performs preliminary actions by pre-building a language model from a corpus of text before actual word choice evaluation. This pre-processing step stores statistical information about word usage patterns, which is then reused during the corruption model analysis. This preliminary action reduces the computational complexity during actual text evaluation, as the system doesn't need to analyze entire corpora each time a word is checked
Solution Approach 2:
The system segments the complex task of contextual word choice evaluation into two separate models: a language model that captures general word usage patterns and a corruption model that specifically evaluates whether a word is used appropriately in context. This segmentation allows each model to be simpler and more specialized, reducing overall system complexity while maintaining high adaptability
Data Source
AI summary
A writing advisor program (20) receives a proposed text in an author's second language (L2) and determines at least one candidate replacement word for a selected word based on a determined language model (p(c)) and a determined corruption model (p(r|c)). The determined language model reflects correct usage of the text in the second language, independent of the native or first language (L1) of the author, based on (L2) corpora. The determined corruption model is based on some a priori knowledge about probable corruption paths leading the author to realize some inadequate expression in the second language instead of the correct, intended expression. Different types of corruption paths may be used that include bidirectional translations, false-friends, synonyms, common semantic features, second language internal cognates, preposition alternatives, and first language inserts.