@inproceedings{thorne-vlachos-2021-elastic,
title = "Elastic weight consolidation for better bias inoculation",
author = "Thorne, James and
Vlachos, Andreas",
booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume",
month = apr,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.eacl-main.82",
doi = "10.18653/v1/2021.eacl-main.82",
pages = "957--964",
abstract = "The biases present in training datasets have been shown to affect models for sentence pair classification tasks such as natural language inference (NLI) and fact verification. While fine-tuning models on additional data has been used to mitigate them, a common issue is that of catastrophic forgetting of the original training dataset. In this paper, we show that elastic weight consolidation (EWC) allows fine-tuning of models to mitigate biases while being less susceptible to catastrophic forgetting. In our evaluation on fact verification and NLI stress tests, we show that fine-tuning with EWC dominates standard fine-tuning, yielding models with lower levels of forgetting on the original (biased) dataset for equivalent gains in accuracy on the fine-tuning (unbiased) dataset.",
}
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%0 Conference Proceedings
%T Elastic weight consolidation for better bias inoculation
%A Thorne, James
%A Vlachos, Andreas
%S Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
%D 2021
%8 apr
%I Association for Computational Linguistics
%C Online
%F thorne-vlachos-2021-elastic
%X The biases present in training datasets have been shown to affect models for sentence pair classification tasks such as natural language inference (NLI) and fact verification. While fine-tuning models on additional data has been used to mitigate them, a common issue is that of catastrophic forgetting of the original training dataset. In this paper, we show that elastic weight consolidation (EWC) allows fine-tuning of models to mitigate biases while being less susceptible to catastrophic forgetting. In our evaluation on fact verification and NLI stress tests, we show that fine-tuning with EWC dominates standard fine-tuning, yielding models with lower levels of forgetting on the original (biased) dataset for equivalent gains in accuracy on the fine-tuning (unbiased) dataset.
%R 10.18653/v1/2021.eacl-main.82
%U https://aclanthology.org/2021.eacl-main.82
%U https://doi.org/10.18653/v1/2021.eacl-main.82
%P 957-964
Markdown (Informal)
[Elastic weight consolidation for better bias inoculation](https://aclanthology.org/2021.eacl-main.82) (Thorne & Vlachos, EACL 2021)
ACL
- James Thorne and Andreas Vlachos. 2021. Elastic weight consolidation for better bias inoculation. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 957–964, Online. Association for Computational Linguistics.