# Rule Augmented Unsupervised Constituency Parsing

arXiv: https://arxiv.org/abs/2105.10193 · HTML: https://mlsanity.com/paper/2105.10193/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 26 references that are in the map.
- First posted on arXiv 21 May 2021.

## Builds on (most cited first, 13 of 13)

- [Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks](https://mlsanity.com/paper/1503.00075/) (2015)
- [Ordered Neurons: Integrating Tree Structures into Recurrent Neural Networks](https://mlsanity.com/paper/1810.09536/) (2018)
- [Compound Probabilistic Context-Free Grammars for Grammar Induction](https://mlsanity.com/paper/1906.10225/) (2019)
- [Unsupervised Latent Tree Induction with Deep Inside-Outside Recursive Autoencoders](https://mlsanity.com/paper/1904.02142/) (2019)
- [Neural Language Modeling by Jointly Learning Syntax and Lexicon](https://mlsanity.com/paper/1711.02013/) (2017)
- [Learning to Compose Task-Specific Tree Structures](https://mlsanity.com/paper/1707.02786/) (2017)
- [Are Pre-trained Language Models Aware of Phrases? Simple but Strong Baselines for Grammar Induction](https://mlsanity.com/paper/2002.00737/) (2020)
- [Learning to Compose Words into Sentences with Reinforcement Learning](https://mlsanity.com/paper/1611.09100/) (2016)
- [Jointly Learning Sentence Embeddings and Syntax with Unsupervised Tree-LSTMs](https://mlsanity.com/paper/1705.09189/) (2017)
- [Learning to Parse and Translate Improves Neural Machine Translation](https://mlsanity.com/paper/1702.03525/) (2017)
- [The Neural Network Pushdown Automaton: Model, Stack and Learning Simulations](https://mlsanity.com/paper/1711.05738/) (2017)
- [Incorporating Syntactic Uncertainty in Neural Machine Translation with Forest-to-Sequence Model](https://mlsanity.com/paper/1711.07019/) (2017)
- [Unsupervised Learning of Explainable Parse Trees for Improved Generalisation](https://mlsanity.com/paper/2104.04998/) (2021)

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

- [Neural Bi-Lexicalized PCFG Induction](https://mlsanity.com/paper/2105.15021/) (2021)
- [SPEAR : Semi-supervised Data Programming in Python](https://mlsanity.com/paper/2108.00373/) (2021)
- [FAIR: Filtering of Automatically Induced Rules](https://mlsanity.com/paper/2402.15472/) (2024)
- [Unsupervised Full Constituency Parsing with Neighboring Distribution Divergence](https://mlsanity.com/paper/2110.15931/) (2021)

## Abstract (arXiv)

Recently, unsupervised parsing of syntactic trees has gained considerable attention. A prototypical approach to such unsupervised parsing employs reinforcement learning and auto-encoders. However, no mechanism ensures that the learnt model leverages the well-understood language grammar. We propose an approach that utilizes very generic linguistic knowledge of the language present in the form of syntactic rules, thus inducing better syntactic structures. We introduce a novel formulation that takes advantage of the syntactic grammar rules and is independent of the base system. We achieve new state-of-the-art results on two benchmarks datasets, MNLI and WSJ. The source code of the paper is available at https://github.com/anshuln/Diora_with_rules.