Table of Contents
Handling out-of-vocabulary (OOV) words is a common challenge in natural language processing. Language models often encounter words they have not seen during training, which can affect their performance. This article discusses practical algorithms used to address this issue effectively.
Subword Tokenization
Subword tokenization breaks words into smaller units, such as prefixes, suffixes, or character sequences. This approach allows models to process unseen words by decomposing them into known subword components. Popular algorithms include Byte Pair Encoding (BPE) and WordPiece.
Character-Level Models
Character-level models operate directly on individual characters rather than words. This method enables the model to handle any new word by analyzing its character sequence. Although computationally intensive, it provides robustness against OOV issues.
Embedding Approximation Techniques
Embedding approximation involves estimating vectors for unseen words based on their subword components or similar known words. Techniques include averaging embeddings of subword units or using context-based inference to generate plausible embeddings for OOV words.
Conclusion
Implementing these algorithms enhances the ability of language models to process out-of-vocabulary words effectively. Combining subword tokenization with embedding approximation often yields the best results in practical applications.