# Inducing Constituency Trees through Neural Machine Translation

arXiv: https://arxiv.org/abs/1909.10056 · HTML: https://mlsanity.com/paper/1909.10056/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Sep 2019.

## Built on by (most cited first, 1 of 1)

- [Leveraging Grammar Induction for Language Understanding and Generation](https://mlsanity.com/paper/2410.04878/) (2024)

## Abstract (arXiv)

Latent tree learning(LTL) methods learn to parse sentences using only indirect supervision from a downstream task. Recent advances in latent tree learning have made it possible to recover moderately high quality tree structures by training with language modeling or auto-encoding objectives. In this work, we explore the hypothesis that decoding in machine translation, as a conditional language modeling task, will produce better tree structures since it offers a similar training signal as language modeling, but with more semantic signal. We adapt two existing latent-tree language models--PRPN andON-LSTM--for use in translation. We find that they indeed recover trees that are better in F1 score than those seen in language modeling on WSJ test set, while maintaining strong translation quality. We observe that translation is a better objective than language modeling for inducing trees, marking the first success at latent tree learning using a machine translation objective. Additionally, our findings suggest that, although translation provides better signal for inducing trees than language modeling, translation models can perform well without exploiting the latent tree structure.