Hybrid Tokenization Framework for Language-Agnostic Intent Classification
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Solution Overview
Problem
Current intent classification technologies face challenges in scalability and consistency across multiple languages, particularly with code-mixed texts and morphologically rich languages, due to language-dependent tokenization techniques that fail to maintain lexical and semantic information effectively.
Innovation Solution
A hybrid tokenization framework that combines white-space and unsupervised tokenization methods, such as SentencePiece, weighted based on language identification and proportion of language present in the training data, to generate a term frequency-inverse document frequency (tf-idf) matrix for language-agnostic intent classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If language-dependent tokenization techniques are used, then tokenization accuracy for specific languages is improved, but scalability across multiple languages and consistency with code-mixed texts deteriorates
Solution Approach 1:
The patent applies universality by creating a language-agnostic tokenization framework that processes multiple languages and code-mixed texts through a single unified approach. The system uses whitespace tokenization combined with unsupervised subword tokenization (SentencePiece) and language identification to handle diverse language inputs without requiring language-specific configurations, thereby achieving both accuracy and scalability across multiple languages.
2Manufacturing precision
If language-specific preprocessing steps are applied, then feature extraction quality for individual languages is improved, but consistency across different languages deteriorates
Solution Approach 1:
The patent applies local quality by adapting the tokenization strategy to the specific characteristics of each language or text segment while maintaining overall framework consistency. The system performs language identification and selects appropriate tokenization methods (whitespace-based for languages with clear word boundaries, unsupervised subword for morphologically rich languages) locally for each input, ensuring high feature extraction quality for each language while maintaining consistent processing pipelines across all languages.
3Reliability
If multiple tokenization methods are combined, then tokenization robustness for code-mixed texts is improved, but system complexity increases
Solution Approach 1:
The patent applies dynamics by implementing a dynamic tokenization system that automatically selects and combines tokenization methods based on the input characteristics. The system performs language identification and adapts the tokenization strategy dynamically - using whitespace tokenization for languages with clear boundaries, unsupervised subword tokenization for morphologically rich languages, and combining both for code-mixed texts. This dynamic adaptation achieves robustness for diverse inputs while managing complexity through automated decision-making rather than manual configuration.
Data Source
AI summary
A system for classifying a language sample intent by receiving a language sample including a set of features, identifying language sample features, determining a tokenization score for the language sample according to the language sample features, eliminating duplicate features according to the tokenization score, determining a term frequency (tf) according to the identified features and the tokenization score, determining an inverse document frequency (idf) according to the identified features and the tokenization score, and generating a term frequency-inverse document frequency (tf-idf) matrix for the identified features.