We propose a hierarchical attention network for document classification. Our model has two distinctive characteristics: (i) it has a hier-archical structure that mirrors the hierarchical structure of documents; (ii) it has two levels of attention mechanisms applied at the word-and sentence-level, enabling it to attend dif-

2025年2月13日 · Hierarchical Attention Networks for Document Classification. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies , pages 1480–1489, San Diego, California.

论文标题： Hierarchical Attention Networks for Document Classification. 原文 传送门： CMU的工作，利用分层注意力网络做文本分类的task，发表在NAACL 2016，目前citation已经接近2500次，可以说是文本分类领域非常有代表性的工作。 这篇论文写的很清晰，有很多intuitive的解释和 empirical 的分析，值得一读！ 文本分类，本质上是Representation learning的问题. 自然语言没 …

2021年6月6日 · With the H-MHSA module incorporated, we build a family of Hierarchical-Attention-based Transformer Networks, namely HAT-Net. To demonstrate the superiority of HAT-Net in scene understanding, we conduct extensive experiments on fundamental vision tasks, including image classification, semantic segmentation, object detection, and instance ...

Hierarchical Attention Networks for Document Classification We know that documents have a hierarchical structure, words combine to form sentences and sentences combine to form documents. We can try to learn that structure or we can input this hierarchical structure into the model and see if it improves the performance of existing models.

This repository contains an implementation of Hierarchical Attention Networks for Document Classification in keras and another implementation of the same network in tensorflow. Hierarchical Attention Networks consists of the following parts: Embedding layer; Word Encoder: word level bi-directional GRU to get rich representation of words

We propose a hierarchical attention network for document classification. Our model has two distinctive characteristics: (i) it has a hierarchical structure that mirrors the hierarchical structure of documents; (ii) it has two levels of attention mechanisms applied at the wordand sentence-level, enabling it to attend differentially to more and ...

2025年2月12日 · We propose combining this approach with the benefits of convolutional filters and a hierarchical structure to create a document classification model that is both highly accurate and fast to train – we name our method Hierarchical Convolutional Attention Networks.

2016年6月8日 · We propose a novel attention network, which accurately attends to target objects of various scales and shapes in images through multiple stages. The proposed network enables multiple layers to estimate attention in a convolutional neural network (CNN).

2023年10月16日 · To address this problem, motivated by the temporal attention mechanism in brains, we propose a hierarchical attention network for learning fine-grained feature representations, called HAN, where the features learnt by implementing a sequence of spatial self-attention operations corresponding to multiple moments are aggregated progressively.