# Jointly Learning Sentence Embeddings and Syntax with Unsupervised Tree-LSTMs

## TL;DR

This paper presents an unsupervised, fully differentiable Tree-LSTM model that jointly learns sentence embeddings and syntax trees, eliminating the need for external parse trees and improving performance on NLP tasks.

## Contribution

It introduces a novel neural network that combines unsupervised tree parsing with sentence embedding learning using a differentiable Tree-LSTM architecture.

## Key findings

- Outperforms supervised Tree-LSTM models on textual entailment
- Achieves better results on reverse dictionary task
- Eliminates dependency on external parse trees

## Abstract

We introduce a neural network that represents sentences by composing their words according to induced binary parse trees. We use Tree-LSTM as our composition function, applied along a tree structure found by a fully differentiable natural language chart parser. Our model simultaneously optimises both the composition function and the parser, thus eliminating the need for externally-provided parse trees which are normally required for Tree-LSTM. It can therefore be seen as a tree-based RNN that is unsupervised with respect to the parse trees. As it is fully differentiable, our model is easily trained with an off-the-shelf gradient descent method and backpropagation. We demonstrate that it achieves better performance compared to various supervised Tree-LSTM architectures on a textual entailment task and a reverse dictionary task.