# Transition-based Bubble Parsing: Improvements on Coordination Structure Prediction

arXiv: https://arxiv.org/abs/2107.06905 · HTML: https://mlsanity.com/paper/2107.06905/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 69 references that are in the map.
- First posted on arXiv 14 Jul 2021.

## Builds on (most cited first, 15 of 15)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Deep Biaffine Attention for Neural Dependency Parsing](https://mlsanity.com/paper/1611.01734/) (2016)
- [Universal Dependencies v2: An Evergrowing Multilingual Treebank Collection](https://mlsanity.com/paper/2004.10643/) (2020)
- [Simple and Accurate Dependency Parsing Using Bidirectional LSTM Feature Representations](https://mlsanity.com/paper/1603.04351/) (2016)
- [Transition-Based Dependency Parsing with Stack Long Short-Term Memory](https://mlsanity.com/paper/1505.08075/) (2015)
- [Graph-to-Graph Transformer for Transition-based Dependency Parsing](https://mlsanity.com/paper/1911.03561/) (2019)
- [Prague Dependency Treebank -- Consolidated 1.0](https://mlsanity.com/paper/2006.03679/) (2020)
- [Fast(er) Exact Decoding and Global Training for Transition-Based Dependency Parsing via a Minimal Feature Set](https://mlsanity.com/paper/1708.09403/) (2017)
- [Recursive Subtree Composition in LSTM-Based Dependency Parsing](https://mlsanity.com/paper/1902.09781/) (2019)
- [Coordinate Constructions in English Enhanced Universal Dependencies: Analysis and Computational Modeling](https://mlsanity.com/paper/2103.08955/) (2021)
- [Coordination Annotation Extension in the Penn Tree Bank](https://mlsanity.com/paper/1606.02529/) (2016)
- [A Neural Network for Coordination Boundary Prediction](https://mlsanity.com/paper/1610.03946/) (2016)
- [Improving a Strong Neural Parser with Conjunction-Specific Features](https://mlsanity.com/paper/1702.06733/) (2017)
- [Syntactic Nuclei in Dependency Parsing -- A Multilingual Exploration](https://mlsanity.com/paper/2101.11959/) (2021)

## Abstract (arXiv)

We propose a transition-based bubble parser to perform coordination structure identification and dependency-based syntactic analysis simultaneously. Bubble representations were proposed in the formal linguistics literature decades ago; they enhance dependency trees by encoding coordination boundaries and internal relationships within coordination structures explicitly. In this paper, we introduce a transition system and neural models for parsing these bubble-enhanced structures. Experimental results on the English Penn Treebank and the English GENIA corpus show that our parsers beat previous state-of-the-art approaches on the task of coordination structure prediction, especially for the subset of sentences with complex coordination structures.