subwordyksiköistä

Subword units, often referred to as subword units or subwords, are fundamental components in various natural language processing (NLP) tasks. They represent a level of granularity between individual characters and full words. Instead of treating words as indivisible tokens, subword units break them down into smaller, meaningful fragments. This approach is particularly useful for handling rare words, out-of-vocabulary words, and morphologically rich languages.

The concept of subword units addresses limitations found in traditional word-based NLP models. When a word not seen during training appears, a word-based model cannot process it effectively. Subword units mitigate this by representing unseen words as a combination of known subword units, allowing the model to infer meaning based on these components. Common techniques for generating subword units include Byte Pair Encoding (BPE), WordPiece, and SentencePiece. BPE iteratively merges the most frequent adjacent character pairs to form new subword units. WordPiece, used in models like BERT, starts with a vocabulary of characters and greedily extends them to form subwords that maximize the likelihood of the training data. SentencePiece treats the input text as a sequence of Unicode characters and learns subword units directly from this sequence, offering flexibility in handling different languages and avoiding pre-tokenization steps.

The use of subword units has significantly improved the performance of NLP models in tasks such as machine translation, text generation, and sentiment analysis. By enabling models to represent and process a wider range of vocabulary and morphological variations, subword units contribute to more robust and accurate language understanding.