Subpalavra embedding is a method used in processamento de linguagem natural (NLP) that breaks down words into smaller components, known as subwords. This approach is particularly useful for handling the vast variety of words and forms in human languages, including rare or unseen words. By representing words as combinations of subwords, models can generalize better and understand the relationships between different word forms.
Nos embeddings tradicionais de palavras, cada palavra é representada como um vetor único em uma espaço de alta dimensão. However, this can lead to challenges when the model encounters out-of-vocabulary (OOV) words—words that were not present in the training dataset. Subword embedding tackles this issue by decomposing words into smaller units, such as prefixes, suffixes, or even character n-grams. For example, the word ‘unhappiness’ can be broken down into subwords like ‘un-‘, ‘happi’, and ‘-ness’. This allows the model to leverage its knowledge of subwords to construct meanings for new or rare words.
Algoritmos populares para gerar embeddings de subpalavras incluem Codificação por Par de Bytes (BPE) and WordPiece. These methods have been successfully used in advanced language models like BERT and GPT, which have significantly improved performance on various NLP tasks, such as translation, sentiment analysis, and text generation.
By using subword embeddings, NLP systems can not only enhance their vocabulary coverage but also improve their ability to capture nuances in language, making them more effective in understanding and gerando texto semelhante ao humano.