Is the Best Better? Bayesian Statistical Model Comparison for Natural Language Processing
Computation and Language
2020-10-08 v1 Machine Learning
Methodology
Abstract
Recent work raises concerns about the use of standard splits to compare natural language processing models. We propose a Bayesian statistical model comparison technique which uses k-fold cross-validation across multiple data sets to estimate the likelihood that one model will outperform the other, or that the two will produce practically equivalent results. We use this technique to rank six English part-of-speech taggers across two data sets and three evaluation metrics.
Cite
@article{arxiv.2010.03088,
title = {Is the Best Better? Bayesian Statistical Model Comparison for Natural Language Processing},
author = {Piotr Szymański and Kyle Gorman},
journal= {arXiv preprint arXiv:2010.03088},
year = {2020}
}
Comments
Accepted to EMNLP2020