Models in the Loop: Aiding Crowdworkers with Generative Annotation Assistants

Max Bartolo, Tristan Thrush, Sebastian Riedel, Pontus Stenetorp, Robin Jia, Douwe Kiela


Abstract
In Dynamic Adversarial Data Collection (DADC), human annotators are tasked with finding examples that models struggle to predict correctly. Models trained on DADC-collected training data have been shown to be more robust in adversarial and out-of-domain settings, and are considerably harder for humans to fool. However, DADC is more time-consuming than traditional data collection and thus more costly per annotated example. In this work, we examine whether we can maintain the advantages of DADC, without incurring the additional cost. To that end, we introduce Generative Annotation Assistants (GAAs), generator-in-the-loop models that provide real-time suggestions that annotators can either approve, modify, or reject entirely. We collect training datasets in twenty experimental settings and perform a detailed analysis of this approach for the task of extractive question answering (QA) for both standard and adversarial data collection. We demonstrate that GAAs provide significant efficiency benefits with over a 30% annotation speed-up, while leading to over a 5x improvement in model fooling rates. In addition, we find that using GAA-assisted training data leads to higher downstream model performance on a variety of question answering tasks over adversarial data collection.
Anthology ID:
2022.naacl-main.275
Volume:
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
July
Year:
2022
Address:
Seattle, United States
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3754–3767
Language:
URL:
https://aclanthology.org/2022.naacl-main.275
DOI:
10.18653/v1/2022.naacl-main.275
Bibkey:
Cite (ACL):
Max Bartolo, Tristan Thrush, Sebastian Riedel, Pontus Stenetorp, Robin Jia, and Douwe Kiela. 2022. Models in the Loop: Aiding Crowdworkers with Generative Annotation Assistants. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 3754–3767, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
Models in the Loop: Aiding Crowdworkers with Generative Annotation Assistants (Bartolo et al., NAACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.naacl-main.275.pdf
Data
AdversarialQAKILTMRQASQuAD