# Is the Best Better? Bayesian Statistical Model Comparison for Natural Language Processing

arXiv: https://arxiv.org/abs/2010.03088 · HTML: https://mlsanity.com/paper/2010.03088/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Oct 2020.

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

- [Transformers Go for the LOLs: Generating (Humourous) Titles from Scientific Abstracts End-to-End](https://mlsanity.com/paper/2212.10522/) (2022)
- [Optimal Size-Performance Tradeoffs: Weighing PoS Tagger Models](https://mlsanity.com/paper/2104.07951/) (2021)
- [Data-driven Model Generalizability in Crosslinguistic Low-resource Morphological Segmentation](https://mlsanity.com/paper/2201.01845/) (2022)
- [How Many Ratings per Item are Necessary for Reliable Significance Testing?](https://mlsanity.com/paper/2412.02968/) (2024)

## Abstract (arXiv)

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.