# The Impact of Edge Displacement Vaserstein Distance on UD Parsing Performance

arXiv: https://arxiv.org/abs/2209.07139 · HTML: https://mlsanity.com/paper/2209.07139/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 75 references that are in the map.
- First posted on arXiv 15 Sep 2022.

## Builds on (most cited first, 13 of 13)

- [Deep Biaffine Attention for Neural Dependency Parsing](https://mlsanity.com/paper/1611.01734/) (2016)
- [We Need to Talk About Random Splits](https://mlsanity.com/paper/2005.00636/) (2020)
- [The SIGMORPHON 2019 Shared Task: Morphological Analysis in Context and Cross-Lingual Transfer for Inflection](https://mlsanity.com/paper/1910.11493/) (2019)
- [Viable Dependency Parsing as Sequence Labeling](https://mlsanity.com/paper/1902.10505/) (2019)
- [Deep Contextualized Word Embeddings in Transition-Based and Graph-Based Dependency Parsing -- A Tale of Two Parsers Revisited](https://mlsanity.com/paper/1908.07397/) (2019)
- [Efficient EUD Parsing](https://mlsanity.com/paper/2006.00838/) (2020)
- [The risks of mixing dependency lengths from sequences of different length](https://mlsanity.com/paper/1304.3841/) (2013)
- [The scarcity of crossing dependencies: a direct outcome of a specific constraint?](https://mlsanity.com/paper/1601.03210/) (2016)
- [Are crossing dependencies really scarce?](https://mlsanity.com/paper/1703.08324/) (2017)
- [Replicating and Extending "Because Their Treebanks Leak": Graph Isomorphism, Covariants, and Parser Performance](https://mlsanity.com/paper/2106.00352/) (2021)
- [Inherent Dependency Displacement Bias of Transition-Based Algorithms](https://mlsanity.com/paper/2003.14282/) (2020)
- [Crossings as a side effect of dependency lengths](https://mlsanity.com/paper/1508.06451/) (2015)
- [On the relation between dependency distance, crossing dependencies, and parsing. Comment on "Dependency distance: a new perspective on syntactic patterns in natural languages" by Haitao Liu et al](https://mlsanity.com/paper/1705.09837/) (2017)

## Abstract (arXiv)

We contribute to the discussion on parsing performance in NLP by introducing a measurement that evaluates the differences between the distributions of edge displacement (the directed distance of edges) seen in training and test data. We hypothesize that this measurement will be related to differences observed in parsing performance across treebanks. We motivate this by building upon previous work and then attempt to falsify this hypothesis by using a number of statistical methods. We establish that there is a statistical correlation between this measurement and parsing performance even when controlling for potential covariants. We then use this to establish a sampling technique that gives us an adversarial and complementary split. This gives an idea of the lower and upper bounds of parsing systems for a given treebank in lieu of freshly sampled data. In a broader sense, the methodology presented here can act as a reference for future correlation-based exploratory work in NLP.