# What Taggers Fail to Learn, Parsers Need the Most

arXiv: https://arxiv.org/abs/2104.01083 · HTML: https://mlsanity.com/paper/2104.01083/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 16 references that are in the map.
- First posted on arXiv 2 Apr 2021.

## Builds on (most cited first, 7 of 7)

- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Deep Biaffine Attention for Neural Dependency Parsing](https://mlsanity.com/paper/1611.01734/) (2016)
- [Learning Word Vectors for 157 Languages](https://mlsanity.com/paper/1802.06893/) (2018)
- [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)
- [On the Frailty of Universal POS Tags for Neural UD Parsers](https://mlsanity.com/paper/2010.01830/) (2020)

## Built on by (most cited first, 2 of 2)

- [Not All Linearizations Are Equally Data-Hungry in Sequence Labeling Parsing](https://mlsanity.com/paper/2108.07556/) (2021)
- [Another Dead End for Morphological Tags? Perturbed Inputs and Parsing](https://mlsanity.com/paper/2305.15119/) (2023)

## Abstract (arXiv)

We present an error analysis of neural UPOS taggers to evaluate why using gold standard tags has such a large positive contribution to parsing performance while using predicted UPOS tags either harms performance or offers a negligible improvement. We evaluate what neural dependency parsers implicitly learn about word types and how this relates to the errors taggers make to explain the minimal impact using predicted tags has on parsers. We also present a short analysis on what contexts result in reductions in tagging performance. We then mask UPOS tags based on errors made by taggers to tease away the contribution of UPOS tags which taggers succeed and fail to classify correctly and the impact of tagging errors.