Attention hiérarchique Réseau (HAN) is a d'apprentissage profond designed for tâches de traitement du langage naturel, 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.
L'architecture se compose de deux composants principaux : attention sur les mots and attention sur les phrases. In the first stage, the model processes words in sentences, applying an mécanisme d'attention 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.
Ensuite, ces phrases 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 est particulièrement avantageux pour des tâches telles que l'analyse de sentiment, la classification de documents, 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.
En résumé, les Réseaux d'Attention Hiérarchique offrent un cadre robuste pour le traitement des données textuelles, améliorant la performance sur diverses tâches de NLP en exploitant la structure du langage.