Are All the Datasets in Benchmark Necessary? A Pilot Study of Dataset Evaluation for Text Classification
Yang Xiao, Jinlan Fu, See-Kiong Ng, Pengfei Liu
Abstract
In this paper, we ask the research question of whether all the datasets in the benchmark are necessary. We approach this by first characterizing the distinguishability of datasets when comparing different systems. Experiments on 9 datasets and 36 systems show that several existing benchmark datasets contribute little to discriminating top-scoring systems, while those less used datasets exhibit impressive discriminative power. We further, taking the text classification task as a case study, investigate the possibility of predicting dataset discrimination based on its properties (e.g., average sentence length). Our preliminary experiments promisingly show that given a sufficient number of training experimental records, a meaningful predictor can be learned to estimate dataset discrimination over unseen datasets. We released all datasets with features explored in this work on DataLab.
BibTeX
@inproceedings{xiao-etal-2022-datasets,
title = "Are All the Datasets in Benchmark Necessary? A Pilot Study of Dataset Evaluation for Text Classification",
author = "Xiao, Yang and
Fu, Jinlan and
Ng, See-Kiong and
Liu, Pengfei",
editor = "Carpuat, Marine and
de Marneffe, Marie-Catherine and
Meza Ruiz, Ivan Vladimir",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-main.213/",
doi = "10.18653/v1/2022.naacl-main.213",
pages = "2930--2941"
}