HRNNs

HRNNs, or Hierarchical Recurrent Neural Networks, are a type of recurrent neural network architecture designed to model sequential data that exhibits hierarchical structure. This means that the data can be broken down into smaller, nested sequences. For example, a document can be seen as a sequence of sentences, and each sentence can be seen as a sequence of words. HRNNs leverage this hierarchical nature by employing multiple layers of recurrent neural networks, where each layer operates on a different level of the hierarchy.

Typically, a lower-level RNN processes the most granular elements of the sequence, such as words in a sentence. The output of this lower-level RNN, which summarizes the information at that level, is then fed as input to a higher-level RNN. This higher-level RNN might process sequences of these summaries, such as sentences within a document. This layered approach allows HRNNs to capture both short-range dependencies within the lower levels and long-range dependencies across the higher levels of the hierarchy.

The key advantage of HRNNs is their ability to effectively model complex temporal dependencies in data that has inherent multi-scale structure. This makes them well-suited for tasks like document summarization, sentiment analysis of long texts, and language modeling where understanding context at different levels of granularity is crucial. By processing information at various levels of abstraction, HRNNs can achieve a more comprehensive understanding of the sequential data compared to standard RNNs that treat the entire sequence as flat.