# Unsupervised Grammar Induction with Depth-bounded PCFG

arXiv: https://arxiv.org/abs/1802.08545 · HTML: https://mlsanity.com/paper/1802.08545/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Feb 2018.

## Built on by (most cited first, 8 of 8)

- [Self-Attention Networks Can Process Bounded Hierarchical Languages](https://mlsanity.com/paper/2105.11115/) (2021)
- [Transformers are uninterpretable with myopic methods: a case study with bounded Dyck grammars](https://mlsanity.com/paper/2312.01429/) (2023)
- [Neural Bi-Lexicalized PCFG Induction](https://mlsanity.com/paper/2105.15021/) (2021)
- [Video-aided Unsupervised Grammar Induction](https://mlsanity.com/paper/2104.04369/) (2021)
- [Improved Latent Tree Induction with Distant Supervision via Span Constraints](https://mlsanity.com/paper/2109.05112/) (2021)
- [Learning Syntax from Naturally-Occurring Bracketings](https://mlsanity.com/paper/2104.13933/) (2021)
- [Learning a Grammar Inducer from Massive Uncurated Instructional Videos](https://mlsanity.com/paper/2210.12309/) (2022)
- [Alpha Discovery via Grammar-Guided Learning and Search](https://mlsanity.com/paper/2601.22119/) (2026)

## Abstract (arXiv)

There has been recent interest in applying cognitively or empirically motivated bounds on recursion depth to limit the search space of grammar induction models (Ponvert et al., 2011; Noji and Johnson, 2016; Shain et al., 2016). This work extends this depth-bounding approach to probabilistic context-free grammar induction (DB-PCFG), which has a smaller parameter space than hierarchical sequence models, and therefore more fully exploits the space reductions of depth-bounding. Results for this model on grammar acquisition from transcribed child-directed speech and newswire text exceed or are competitive with those of other models when evaluated on parse accuracy. Moreover, gram- mars acquired from this model demonstrate a consistent use of category labels, something which has not been demonstrated by other acquisition models.