# Unsupervised and Few-shot Parsing from Pretrained Language Models

arXiv: https://arxiv.org/abs/2206.04980 · HTML: https://mlsanity.com/paper/2206.04980/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 24 of its 51 references that are in the map.
- First posted on arXiv 10 Jun 2022.

## Builds on (most cited first, 24 of 24)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [BERT Rediscovers the Classical NLP Pipeline](https://mlsanity.com/paper/1905.05950/) (2019)
- [What Does BERT Look At? An Analysis of BERT's Attention](https://mlsanity.com/paper/1906.04341/) (2019)
- [Constituency Parsing with a Self-Attentive Encoder](https://mlsanity.com/paper/1805.01052/) (2018)
- [Multilingual Constituency Parsing with Self-Attention and Pre-Training](https://mlsanity.com/paper/1812.11760/) (2018)
- [Ordered Neurons: Integrating Tree Structures into Recurrent Neural Networks](https://mlsanity.com/paper/1810.09536/) (2018)
- [Do Attention Heads in BERT Track Syntactic Dependencies?](https://mlsanity.com/paper/1911.12246/) (2019)
- [Compound Probabilistic Context-Free Grammars for Grammar Induction](https://mlsanity.com/paper/1906.10225/) (2019)
- [Tree Transformer: Integrating Tree Structures into Self-Attention](https://mlsanity.com/paper/1909.06639/) (2019)
- [Unsupervised Latent Tree Induction with Deep Inside-Outside Recursive Autoencoders](https://mlsanity.com/paper/1904.02142/) (2019)
- [A Minimal Span-Based Neural Constituency Parser](https://mlsanity.com/paper/1705.03919/) (2017)
- [Neural Language Modeling by Jointly Learning Syntax and Lexicon](https://mlsanity.com/paper/1711.02013/) (2017)
- [Unsupervised Recurrent Neural Network Grammars](https://mlsanity.com/paper/1904.03746/) (2019)
- [Are Pre-trained Language Models Aware of Phrases? Simple but Strong Baselines for Grammar Induction](https://mlsanity.com/paper/2002.00737/) (2020)
- [Straight to the Tree: Constituency Parsing with Neural Syntactic Distance](https://mlsanity.com/paper/1806.04168/) (2018)
- [Parsing as Pretraining](https://mlsanity.com/paper/2002.01685/) (2020)
- [On the Role of Supervision in Unsupervised Constituency Parsing](https://mlsanity.com/paper/2010.02423/) (2020)
- [Video-aided Unsupervised Grammar Induction](https://mlsanity.com/paper/2104.04369/) (2021)
- [Modeling Source Syntax for Neural Machine Translation](https://mlsanity.com/paper/1705.01020/) (2017)
- [Heads-up! Unsupervised Constituency Parsing via Self-Attention Heads](https://mlsanity.com/paper/2010.09517/) (2020)
- [A Critical Analysis of Biased Parsers in Unsupervised Parsing](https://mlsanity.com/paper/1909.09428/) (2019)
- [Synthesizing Context-free Grammars from Recurrent Neural Networks (Extended Version)](https://mlsanity.com/paper/2101.08200/) (2021)

## Abstract (arXiv)

Pretrained language models are generally acknowledged to be able to encode syntax [Tenney et al., 2019, Jawahar et al., 2019, Hewitt and Manning, 2019]. In this article, we propose UPOA, an Unsupervised constituent Parsing model that calculates an Out Association score solely based on the self-attention weight matrix learned in a pretrained language model as the syntactic distance for span segmentation. We further propose an enhanced version, UPIO, which exploits both inside association and outside association scores for estimating the likelihood of a span. Experiments with UPOA and UPIO disclose that the linear projection matrices for the query and key in the self-attention mechanism play an important role in parsing. We therefore extend the unsupervised models to few-shot parsing models (FPOA, FPIO) that use a few annotated trees to learn better linear projection matrices for parsing. Experiments on the Penn Treebank demonstrate that our unsupervised parsing model UPIO achieves results comparable to the state of the art on short sentences (length <= 10). Our few-shot parsing model FPIO trained with only 20 annotated trees outperforms a previous few-shot parsing method trained with 50 annotated trees. Experiments on cross-lingual parsing show that both unsupervised and few-shot parsing methods are better than previous methods on most languages of SPMRL [Seddah et al., 2013].