# CRF Autoencoder for Unsupervised Dependency Parsing

arXiv: https://arxiv.org/abs/1708.01018 · HTML: https://mlsanity.com/paper/1708.01018/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Aug 2017.

## Built on by (most cited first, 6 of 6)

- [Semantic Role Labeling as Dependency Parsing: Exploring Latent Tree Structures Inside Arguments](https://mlsanity.com/paper/2110.06865/) (2021)
- [Recent Advances in Neural Text Generation: A Task-Agnostic Survey](https://mlsanity.com/paper/2203.03047/) (2022)
- [Unsupervised Vision-Language Parsing: Seamlessly Bridging Visual Scene Graphs with Language Structures via Dependency Relationships](https://mlsanity.com/paper/2203.14260/) (2022)
- [Dependency Induction Through the Lens of Visual Perception](https://mlsanity.com/paper/2109.09790/) (2021)
- [Scaling Structured Inference with Randomization](https://mlsanity.com/paper/2112.03638/) (2021)
- [Bridging Pre-trained Language Models and Hand-crafted Features for Unsupervised POS Tagging](https://mlsanity.com/paper/2203.10315/) (2022)

## Abstract (arXiv)

Unsupervised dependency parsing, which tries to discover linguistic dependency structures from unannotated data, is a very challenging task. Almost all previous work on this task focuses on learning generative models. In this paper, we develop an unsupervised dependency parsing model based on the CRF autoencoder. The encoder part of our model is discriminative and globally normalized which allows us to use rich features as well as universal linguistic priors. We propose an exact algorithm for parsing as well as a tractable learning algorithm. We evaluated the performance of our model on eight multilingual treebanks and found that our model achieved comparable performance with state-of-the-art approaches.