How Does Counterfactually Augmented Data Impact Models for Social Computing Constructs?

Indira Sen, Mattia Samory, Fabian Flöck, Claudia Wagner, Isabelle Augenstein


Abstract
As NLP models are increasingly deployed in socially situated settings such as online abusive content detection, it is crucial to ensure that these models are robust. One way of improving model robustness is to generate counterfactually augmented data (CAD) for training models that can better learn to distinguish between core features and data artifacts. While models trained on this type of data have shown promising out-of-domain generalizability, it is still unclear what the sources of such improvements are. We investigate the benefits of CAD for social NLP models by focusing on three social computing constructs — sentiment, sexism, and hate speech. Assessing the performance of models trained with and without CAD across different types of datasets, we find that while models trained on CAD show lower in-domain performance, they generalize better out-of-domain. We unpack this apparent discrepancy using machine explanations and find that CAD reduces model reliance on spurious features. Leveraging a novel typology of CAD to analyze their relationship with model performance, we find that CAD which acts on the construct directly or a diverse set of CAD leads to higher performance.
Anthology ID:
2021.emnlp-main.28
Volume:
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
Month:
November
Year:
2021
Address:
Online and Punta Cana, Dominican Republic
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
325–344
Language:
URL:
https://aclanthology.org/2021.emnlp-main.28
DOI:
10.18653/v1/2021.emnlp-main.28
Bibkey:
Cite (ACL):
Indira Sen, Mattia Samory, Fabian Flöck, Claudia Wagner, and Isabelle Augenstein. 2021. How Does Counterfactually Augmented Data Impact Models for Social Computing Constructs?. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 325–344, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
How Does Counterfactually Augmented Data Impact Models for Social Computing Constructs? (Sen et al., EMNLP 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.emnlp-main.28.pdf
Video:
 https://aclanthology.org/2021.emnlp-main.28.mp4
Code
 gesiscss/socialcad