Hierarchical Attention Network (HAN) is a deep learning architecture designed for natural language processing tasks, 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.
The architecture consists of two main components: word attention and sentence attention. In the first stage, the model processes words in sentences, applying an attention mechanism 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.
Next, these sentence 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 is particularly advantageous in tasks such as sentiment analysis, document classification, 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.
In summary, Hierarchical Attention Networks provide a robust framework for processing textual data, improving performance on various NLP tasks by leveraging the structure of language.