Cross-lingual Text Classification via Character Embeddings
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Solution Overview
Problem
Existing text classification techniques face challenges in efficiently classifying complex texts across multiple languages, often requiring separate models and increased computational resources, which limits their scalability and efficiency.
Innovation Solution
A convolutional neural network-based text classification platform that uses character-level embeddings, stacked convolution layers, and max over-time pooling to classify text agnostically across languages, reducing the need for language-specific models and minimizing computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If separate language-specific models are used for text classification, then classification accuracy for each language is improved, but device complexity and computational resources increase
Solution Approach 1:
The patent implements a universal text classification model that processes multiple languages through a single architecture. The character embedding layer creates language-agnostic representations, allowing the same convolutional neural network to classify texts in different languages without requiring separate language-specific models, thus reducing device complexity while maintaining classification capability across languages
Solution Approach 2:
The patent changes the parameter representation by using character-level embeddings instead of word-level representations. This parameter transformation enables the model to capture language-specific patterns through learned embeddings while maintaining a unified model structure, resolving the contradiction between accuracy and complexity
2Measurement precision
If separate language-specific models are used for text classification, then language-specific classification performance is improved, but computational resources and processing time increase
Solution Approach 1:
The patent merges multiple language processing capabilities into a single unified model. By combining character embeddings from different languages into a shared vector space and processing them through the same convolutional layers, the system reduces computational overhead while maintaining the ability to classify multiple languages effectively
Solution Approach 2:
The unified model serves multiple language classification functions simultaneously, reducing the total computational resources required compared to running separate models for each language. The single model is trained on multilingual data and can classify texts in different languages without requiring separate computational pipelines
3Adaptability or versatility
If character-level embeddings with stacked convolution layers are used, then cross-lingual classification capability is improved, but device complexity increases
Solution Approach 1:
The patent segments the text processing into character-level units and processes them through embedding layers followed by stacked convolutional layers. This segmentation approach allows the model to capture local patterns and language-specific features at the character level while maintaining a relatively simple overall architecture that can be extended to multiple languages
Solution Approach 2:
The patent transitions from word-level to character-level representation, adding a dimensional transformation that enables cross-lingual processing. By operating at the character level, the model can learn language-agnostic features while the stacked convolution layers capture hierarchical patterns, achieving cross-lingual capability without proportionally increasing complexity
4Device complexity
If a unified cross-lingual model is used, then device complexity is reduced, but measurement precision for language-specific tasks may worsen
Solution Approach 1:
The patent transforms the representation parameters by using character-level embeddings that are language-agnostic. This parameter change allows the unified model to adapt to different languages through learned embeddings while maintaining a simple architecture, achieving a balance between model simplicity and language-specific accuracy through data-driven parameter learning
Data Source
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AI summary
A device may be configured to obtain text from a document. The device may perform embedding to obtain a data structure indicating probabilities associated with characters included in the text and apply a first convolution to the data structure to obtain different representations of the characters included in the text. In addition, the device may apply parallel convolution to the different representations to obtain multiple sets of character representations, subsample the multiple sets of character representations, and pool the subsampled multiple sets of character representations into a merged data structure. The device may provide the merged data structure to a fully connected layer, of a convolutional neural network, to produce data representing features of the text; and provide the data representing features of the text to an inference layer, of the convolutional neural network, that provides data indicating a classification for the text.