# Learning to Compose over Tree Structures via POS Tags

arXiv: https://arxiv.org/abs/1808.06075 · HTML: https://mlsanity.com/paper/1808.06075/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Aug 2018.

## Built on by (most cited first, 4 of 4)

- [DEIM: An effective deep encoding and interaction model for sentence matching](https://mlsanity.com/paper/2203.10482/) (2022)
- [Recognizing semantic relation in sentence pairs using Tree-RNNs and Typed dependencies](https://mlsanity.com/paper/2201.04810/) (2022)
- [KETM:A Knowledge-Enhanced Text Matching method](https://mlsanity.com/paper/2308.06235/) (2023)
- [Know Yourself and Know Your Neighbour: A Syntactically Informed Self-Supervised Compositional Sentence Representation Learning Framework using a Recursive Hypernetwork](https://mlsanity.com/paper/2610.16547/) (2026)

## Abstract (arXiv)

Recursive Neural Network (RecNN), a type of models which compose words or phrases recursively over syntactic tree structures, has been proven to have superior ability to obtain sentence representation for a variety of NLP tasks. However, RecNN is born with a thorny problem that a shared compositional function for each node of trees can't capture the complex semantic compositionality so that the expressive power of model is limited. In this paper, in order to address this problem, we propose Tag-Guided HyperRecNN/TreeLSTM (TG-HRecNN/TreeLSTM), which introduces hypernetwork into RecNNs to take as inputs Part-of-Speech (POS) tags of word/phrase and generate the semantic composition parameters dynamically. Experimental results on five datasets for two typical NLP tasks show proposed models both obtain significant improvement compared with RecNN and TreeLSTM consistently. Our TG-HTreeLSTM outperforms all existing RecNN-based models and achieves or is competitive with state-of-the-art on four sentence classification benchmarks. The effectiveness of our models is also demonstrated by qualitative analysis.