Atenção Hierárquica Rede (HAN) is a arquitetura de aprendizado profundo designed for tarefas de processamento de linguagem natural, particularly effective in handling long documents and text classification. Unlike traditional models that treat all text equally, HAN employs a hierarchical structure that processes text at multiple levels, allowing it to capture both word-level and sentence-level features.
A arquitetura consiste em dois componentes principais: atenção às palavras and atenção às sentenças. In the first stage, the model processes words in sentences, applying an mecanismo de atenção that weighs the importance of each word relative to the sentence context. This enables the model to focus on significant words while generating sentence representations.
Em seguida, essas frases embeddings are fed into a second attention mechanism that evaluates the importance of each sentence within the document. This hierarchical approach allows the model to effectively summarize the content, capturing critical information while discarding less relevant details.
HAN é particularmente vantajoso em tarefas como análise de sentimento, classificação de documentos, and summarization, as it efficiently handles the complexities of language by modeling the hierarchical nature of text. The inclusion of attention mechanisms enhances interpretability, allowing users to understand which words and sentences influenced the model’s predictions.
Em resumo, as Hierarchical Attention Networks fornecem uma estrutura robusta para o processamento de dados textuais, melhorando o desempenho em várias tarefas de PLN ao aproveitar a estrutura da linguagem.