# Learning to Search for Dependencies

arXiv: https://arxiv.org/abs/1503.05615 · HTML: https://mlsanity.com/paper/1503.05615/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Mar 2015.

## Built on by (most cited first, 3 of 3)

- [Provably Efficient Imitation Learning from Observation Alone](https://mlsanity.com/paper/1905.10948/) (2019)
- [Mitigating Covariate Shift in Imitation Learning via Offline Data Without Great Coverage](https://mlsanity.com/paper/2106.03207/) (2021)
- [Linguistically-Informed Neural Architectures for Lexical, Syntactic and Semantic Tasks in Sanskrit](https://mlsanity.com/paper/2308.08807/) (2023)

## Abstract (arXiv)

We demonstrate that a dependency parser can be built using a credit assignment compiler which removes the burden of worrying about low-level machine learning details from the parser implementation. The result is a simple parser which robustly applies to many languages that provides similar statistical and computational performance with best-to-date transition-based parsing approaches, while avoiding various downsides including randomization, extra feature requirements, and custom learning algorithms.