# Neural Tree Indexers for Text Understanding

arXiv: https://arxiv.org/abs/1607.04492 · HTML: https://mlsanity.com/paper/1607.04492/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jul 2016.

## Built on by (most cited first, 9 of 9)

- [Attention, please! A survey of Neural Attention Models in Deep Learning](https://mlsanity.com/paper/2103.16775/) (2021)
- [Modeling Hierarchical Structures with Continuous Recursive Neural Networks](https://mlsanity.com/paper/2106.06038/) (2021)
- [Beam Tree Recursive Cells](https://mlsanity.com/paper/2305.19999/) (2023)
- [Recursion in Recursion: Two-Level Nested Recursion for Length Generalization with Scalability](https://mlsanity.com/paper/2311.04449/) (2023)
- [Efficient Beam Tree Recursion](https://mlsanity.com/paper/2307.10779/) (2023)
- [Deep Learning-based Sentiment Classification: A Comparative Survey](https://mlsanity.com/paper/2312.17253/) (2023)
- [Unsupervised Learning of Explainable Parse Trees for Improved Generalisation](https://mlsanity.com/paper/2104.04998/) (2021)
- [Length Generalization with Log-Depth Recurrent Units](https://mlsanity.com/paper/2605.26035/) (2026)
- [Auditable Unit-Aware Thresholds in Symbolic Regression via Logistic-Gated Operators](https://mlsanity.com/paper/2510.05178/) (2025)

## Abstract (arXiv)

Recurrent neural networks (RNNs) process input text sequentially and model the conditional transition between word tokens. In contrast, the advantages of recursive networks include that they explicitly model the compositionality and the recursive structure of natural language. However, the current recursive architecture is limited by its dependence on syntactic tree. In this paper, we introduce a robust syntactic parsing-independent tree structured model, Neural Tree Indexers (NTI) that provides a middle ground between the sequential RNNs and the syntactic treebased recursive models. NTI constructs a full n-ary tree by processing the input text with its node function in a bottom-up fashion. Attention mechanism can then be applied to both structure and node function. We implemented and evaluated a binarytree model of NTI, showing the model achieved the state-of-the-art performance on three different NLP tasks: natural language inference, answer sentence selection, and sentence classification, outperforming state-of-the-art recurrent and recursive neural networks.