# On Parsing as Tagging

arXiv: https://arxiv.org/abs/2211.07344 · HTML: https://mlsanity.com/paper/2211.07344/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 41 references that are in the map.
- First posted on arXiv 14 Nov 2022.

## Builds on (most cited first, 12 of 12)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [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)
- [Head-Driven Phrase Structure Grammar Parsing on Penn Treebank](https://mlsanity.com/paper/1907.02684/) (2019)
- [Viable Dependency Parsing as Sequence Labeling](https://mlsanity.com/paper/1902.10505/) (2019)
- [Constituent Parsing as Sequence Labeling](https://mlsanity.com/paper/1810.08994/) (2018)
- [In-Order Transition-based Constituent Parsing](https://mlsanity.com/paper/1707.05000/) (2017)
- [Scheduled Multi-Task Learning: From Syntax to Translation](https://mlsanity.com/paper/1804.08915/) (2018)
- [Tetra-Tagging: Word-Synchronous Parsing with Linear-Time Inference](https://mlsanity.com/paper/1904.09745/) (2019)
- [A Unifying Theory of Transition-based and Sequence Labeling Parsing](https://mlsanity.com/paper/2011.00584/) (2020)
- [Better, Faster, Stronger Sequence Tagging Constituent Parsers](https://mlsanity.com/paper/1902.10985/) (2019)
- [Algorithms for Weighted Pushdown Automata](https://mlsanity.com/paper/2210.06884/) (2022)

## Abstract (arXiv)

There have been many proposals to reduce constituency parsing to tagging in the literature. To better understand what these approaches have in common, we cast several existing proposals into a unifying pipeline consisting of three steps: linearization, learning, and decoding. In particular, we show how to reduce tetratagging, a state-of-the-art constituency tagger, to shift--reduce parsing by performing a right-corner transformation on the grammar and making a specific independence assumption. Furthermore, we empirically evaluate our taxonomy of tagging pipelines with different choices of linearizers, learners, and decoders. Based on the results in English and a set of 8 typologically diverse languages, we conclude that the linearization of the derivation tree and its alignment with the input sequence is the most critical factor in achieving accurate taggers.