# Coordinate Constructions in English Enhanced Universal Dependencies: Analysis and Computational Modeling

arXiv: https://arxiv.org/abs/2103.08955 · HTML: https://mlsanity.com/paper/2103.08955/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 34 references that are in the map.
- First posted on arXiv 16 Mar 2021.

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

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [HuggingFace's Transformers: State-of-the-art Natural Language Processing](https://mlsanity.com/paper/1910.03771/) (2019)
- [BERT Rediscovers the Classical NLP Pipeline](https://mlsanity.com/paper/1905.05950/) (2019)
- [Deep Biaffine Attention for Neural Dependency Parsing](https://mlsanity.com/paper/1611.01734/) (2016)
- [75 Languages, 1 Model: Parsing Universal Dependencies Universally](https://mlsanity.com/paper/1904.02099/) (2019)
- [Simpler but More Accurate Semantic Dependency Parsing](https://mlsanity.com/paper/1807.01396/) (2018)
- [Universal Dependency Parsing from Scratch](https://mlsanity.com/paper/1901.10457/) (2019)
- [ConjNLI: Natural Language Inference Over Conjunctive Sentences](https://mlsanity.com/paper/2010.10418/) (2020)
- [Efficient EUD Parsing](https://mlsanity.com/paper/2006.00838/) (2020)
- [Enhanced Universal Dependency Parsing with Second-Order Inference and Mixture of Training Data](https://mlsanity.com/paper/2006.01414/) (2020)
- [Sentences with Gapping: Parsing and Reconstructing Elided Predicates](https://mlsanity.com/paper/1804.06922/) (2018)
- [Kopsala: Transition-Based Graph Parsing via Efficient Training and Effective Encoding](https://mlsanity.com/paper/2005.12094/) (2020)

## Built on by (most cited first, 4 of 4)

- [MUX-PLMs: Data Multiplexing for High-throughput Language Models](https://mlsanity.com/paper/2302.12441/) (2023)
- [TGIF: Tree-Graph Integrated-Format Parser for Enhanced UD with Two-Stage Generic- to Individual-Language Finetuning](https://mlsanity.com/paper/2107.06907/) (2021)
- [Cross-linguistically Consistent Semantic and Syntactic Annotation of Child-directed Speech](https://mlsanity.com/paper/2109.10952/) (2021)
- [Transition-based Bubble Parsing: Improvements on Coordination Structure Prediction](https://mlsanity.com/paper/2107.06905/) (2021)

## Abstract (arXiv)

In this paper, we address the representation of coordinate constructions in Enhanced Universal Dependencies (UD), where relevant dependency links are propagated from conjunction heads to other conjuncts. English treebanks for enhanced UD have been created from gold basic dependencies using a heuristic rule-based converter, which propagates only core arguments. With the aim of determining which set of links should be propagated from a semantic perspective, we create a large-scale dataset of manually edited syntax graphs. We identify several systematic errors in the original data, and propose to also propagate adjuncts. We observe high inter-annotator agreement for this semantic annotation task. Using our new manually verified dataset, we perform the first principled comparison of rule-based and (partially novel) machine-learning based methods for conjunction propagation for English. We show that learning propagation rules is more effective than hand-designing heuristic rules. When using automatic parses, our neural graph-parser based edge predictor outperforms the currently predominant pipelinesusing a basic-layer tree parser plus converters.