How Emotionally Stable is ALBERT? Testing Robustness with Stochastic Weight Averaging on a Sentiment Analysis Task

Urja Khurana, Eric Nalisnick, Antske Fokkens


Abstract
Despite their success, modern language models are fragile. Even small changes in their training pipeline can lead to unexpected results. We study this phenomenon by examining the robustness of ALBERT (Lan et al., 2020) in combination with Stochastic Weight Averaging (SWA)—a cheap way of ensembling—on a sentiment analysis task (SST-2). In particular, we analyze SWA’s stability via CheckList criteria (Ribeiro et al., 2020), examining the agreement on errors made by models differing only in their random seed. We hypothesize that SWA is more stable because it ensembles model snapshots taken along the gradient descent trajectory. We quantify stability by comparing the models’ mistakes with Fleiss’ Kappa (Fleiss, 1971) and overlap ratio scores. We find that SWA reduces error rates in general; yet the models still suffer from their own distinct biases (according to CheckList).
Anthology ID:
2021.eval4nlp-1.3
Volume:
Proceedings of the 2nd Workshop on Evaluation and Comparison of NLP Systems
Month:
November
Year:
2021
Address:
Punta Cana, Dominican Republic
Venues:
EMNLP | Eval4NLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
16–31
Language:
URL:
https://aclanthology.org/2021.eval4nlp-1.3
DOI:
10.18653/v1/2021.eval4nlp-1.3
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2021.eval4nlp-1.3.pdf
Data
SST