Character-Based Neural Network for Out-of-Vocabulary Language Processing
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Solution Overview
Problem
Existing language models face challenges in processing out-of-vocabulary words in open-vocabulary environments, where maintaining the previous structure is difficult and the model size increases significantly with a large number of words, making it inefficient for real-time applications.
Innovation Solution
A neural network-based language processing method using an encoder, word estimator, and decoder to generate feature vectors representing input and predicted words, allowing for dynamic adaptation to new words without structural changes, by integrating recurrent neural networks and utilizing a character-based encoder-decoder model to predict words based on character relationships.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the language model structure is changed to process out-of-vocabulary words, then the ability to handle new words is improved, but the model size increases and previous structure cannot be maintained
Solution Approach 1:
The patent segments words into character-level units and processes them through separate encoder and decoder networks. This allows the model to handle any word composition without requiring the entire vocabulary to be pre-defined, resolving the contradiction between handling out-of-vocabulary words and maintaining manageable model size.
Solution Approach 2:
The patent transitions from word-level processing to character-level processing, adding a dimensional transformation. By encoding characters into feature vectors and decoding them back to characters, the model can process any word composition without expanding vocabulary size, thus handling out-of-vocabulary words while maintaining model size.
2Adaptability or versatility
If the vocabulary size increases to cover more words, then the coverage of predictable words is improved, but the operation time increases making it inefficient for real-time applications
Solution Approach 1:
The patent changes the fundamental parameter from vocabulary size to character set size. Instead of expanding vocabulary to cover more words, the model uses a fixed character set to encode and decode any word, dramatically reducing operation time while maintaining unlimited vocabulary coverage capability.
Solution Approach 2:
The patent creates compressed representations (feature vectors) of words through encoding, storing only the essential character-level features rather than full word information. This allows rapid processing and reconstruction of words without requiring large vocabulary tables, improving operation speed while maintaining coverage.
Data Source
AI summary
A language processing method and apparatus is disclosed. The language processing method includes obtaining, using an encoder, a first feature vector representing an input word based on an input sequence of first characters included in the input word. The method also generates, using a word estimator, a second feature vector representing a predicted word associated with the input word by processing the first feature vector using a language model, and decodes, using a decoder, the second feature vector to an output sequence of second characters included in the predicted word using the second feature vector.