# Parsing linearizations appreciate PoS tags - but some are fussy about errors

arXiv: https://arxiv.org/abs/2210.15219 · HTML: https://mlsanity.com/paper/2210.15219/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 23 references that are in the map.
- First posted on arXiv 27 Oct 2022.

## Builds on (most cited first, 11 of 11)

- [Viable Dependency Parsing as Sequence Labeling](https://mlsanity.com/paper/1902.10505/) (2019)
- [Bracketing Encodings for 2-Planar Dependency Parsing](https://mlsanity.com/paper/2011.00596/) (2020)
- [A Unifying Theory of Transition-based and Sequence Labeling Parsing](https://mlsanity.com/paper/2011.00584/) (2020)
- [Improved Transition-Based Parsing by Modeling Characters instead of Words with LSTMs](https://mlsanity.com/paper/1508.00657/) (2015)
- [An Investigation of the Interactions Between Pre-Trained Word Embeddings, Character Models and POS Tags in Dependency Parsing](https://mlsanity.com/paper/1808.09060/) (2018)
- [Is POS Tagging Necessary or Even Helpful for Neural Dependency Parsing?](https://mlsanity.com/paper/2003.03204/) (2020)
- [A Modest Pareto Optimisation Analysis of Dependency Parsers in 2021](https://mlsanity.com/paper/2106.04216/) (2021)
- [Weakly Supervised POS Taggers Perform Poorly on Truly Low-Resource Languages](https://mlsanity.com/paper/2004.13305/) (2020)
- [Not All Linearizations Are Equally Data-Hungry in Sequence Labeling Parsing](https://mlsanity.com/paper/2108.07556/) (2021)
- [A Falta de Pan, Buenas Son Tortas: The Efficacy of Predicted UPOS Tags for Low Resource UD Parsing](https://mlsanity.com/paper/2106.04222/) (2021)
- [On the Frailty of Universal POS Tags for Neural UD Parsers](https://mlsanity.com/paper/2010.01830/) (2020)

## Built on by (most cited first, 1 of 1)

- [Another Dead End for Morphological Tags? Perturbed Inputs and Parsing](https://mlsanity.com/paper/2305.15119/) (2023)

## Abstract (arXiv)

PoS tags, once taken for granted as a useful resource for syntactic parsing, have become more situational with the popularization of deep learning. Recent work on the impact of PoS tags on graph- and transition-based parsers suggests that they are only useful when tagging accuracy is prohibitively high, or in low-resource scenarios. However, such an analysis is lacking for the emerging sequence labeling parsing paradigm, where it is especially relevant as some models explicitly use PoS tags for encoding and decoding. We undertake a study and uncover some trends. Among them, PoS tags are generally more useful for sequence labeling parsers than for other paradigms, but the impact of their accuracy is highly encoding-dependent, with the PoS-based head-selection encoding being best only when both tagging accuracy and resource availability are high.