# Learning from Non-Binary Constituency Trees via Tensor Decomposition

arXiv: https://arxiv.org/abs/2011.00860 · HTML: https://mlsanity.com/paper/2011.00860/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Nov 2020.

## Built on by (most cited first, 3 of 3)

- [High-Order Pooling for Graph Neural Networks with Tensor Decomposition](https://mlsanity.com/paper/2205.11691/) (2022)
- [Tree-Constrained Graph Neural Networks For Argument Mining](https://mlsanity.com/paper/2110.00124/) (2021)
- [Lying Graph Convolution: Learning to Lie for Node Classification Tasks](https://mlsanity.com/paper/2405.01247/) (2024)

## Abstract (arXiv)

Processing sentence constituency trees in binarised form is a common and popular approach in literature. However, constituency trees are non-binary by nature. The binarisation procedure changes deeply the structure, furthering constituents that instead are close. In this work, we introduce a new approach to deal with non-binary constituency trees which leverages tensor-based models. In particular, we show how a powerful composition function based on the canonical tensor decomposition can exploit such a rich structure. A key point of our approach is the weight sharing constraint imposed on the factor matrices, which allows limiting the number of model parameters. Finally, we introduce a Tree-LSTM model which takes advantage of this composition function and we experimentally assess its performance on different NLP tasks.