# Recurrent Neural Networks with Mixed Hierarchical Structures for Natural Language Processing

arXiv: https://arxiv.org/abs/2106.02562 · HTML: https://mlsanity.com/paper/2106.02562/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 15 references that are in the map.
- First posted on arXiv 4 Jun 2021.

## Builds on (most cited first, 7 of 7)

- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [A Convolutional Neural Network for Modelling Sentences](https://mlsanity.com/paper/1404.2188/) (2014)
- [Hierarchical Multiscale Recurrent Neural Networks](https://mlsanity.com/paper/1609.01704/) (2016)
- [A Clockwork RNN](https://mlsanity.com/paper/1402.3511/) (2014)
- [A Hierarchical Recurrent Encoder-Decoder For Generative Context-Aware Query Suggestion](https://mlsanity.com/paper/1507.02221/) (2015)
- [Character-based Neural Machine Translation](https://mlsanity.com/paper/1603.00810/) (2016)

## Built on by (most cited first, 2 of 2)

- [Between words and characters: A Brief History of Open-Vocabulary Modeling and Tokenization in NLP](https://mlsanity.com/paper/2112.10508/) (2021)
- [Recurrent Neural Networks with Mixed Hierarchical Structures and EM Algorithm for Natural Language Processing](https://mlsanity.com/paper/2201.08919/) (2022)

## Abstract (arXiv)

Hierarchical structures exist in both linguistics and Natural Language Processing (NLP) tasks. How to design RNNs to learn hierarchical representations of natural languages remains a long-standing challenge. In this paper, we define two different types of boundaries referred to as static and dynamic boundaries, respectively, and then use them to construct a multi-layer hierarchical structure for document classification tasks. In particular, we focus on a three-layer hierarchical structure with static word- and sentence- layers and a dynamic phrase-layer. LSTM cells and two boundary detectors are used to implement the proposed structure, and the resulting network is called the em Recurrent Neural Network with Mixed Hierarchical Structures (MHS-RNN). We further add three layers of attention mechanisms to the MHS-RNN model. Incorporating attention mechanisms allows our model to use more important content to construct document representation and enhance its performance on document classification tasks. Experiments on five different datasets show that the proposed architecture outperforms previous methods on all the five tasks.