The Impact of Edge Displacement Vaserstein Distance on UD Parsing Performance
This work addresses parsing performance evaluation in NLP, but it is incremental as it builds on existing methods to propose a new measurement and sampling technique.
The paper tackles the problem of evaluating parsing performance differences across treebanks by introducing a measurement based on edge displacement distributions, finding a statistical correlation with parsing performance and using it to establish adversarial and complementary splits to estimate lower and upper bounds.
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.