Iterative Feature Mining for Constraint-Based Data Collection to Increase Data Diversity and Model Robustness

Stefan Larson, Anthony Zheng, Anish Mahendran, Rishi Tekriwal, Adrian Cheung, Eric Guldan, Kevin Leach, Jonathan K. Kummerfeld


Abstract
Diverse data is crucial for training robust models, but crowdsourced text often lacks diversity as workers tend to write simple variations from prompts. We propose a general approach for guiding workers to write more diverse text by iteratively constraining their writing. We show how prior workflows are special cases of our approach, and present a way to apply the approach to dialog tasks such as intent classification and slot-filling. Using our method, we create more challenging versions of test sets from prior dialog datasets and find dramatic performance drops for standard models. Finally, we show that our approach is complementary to recent work on improving data diversity, and training on data collected with our approach leads to more robust models.
Anthology ID:
2020.emnlp-main.650
Volume:
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Month:
November
Year:
2020
Address:
Online
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
8097–8106
Language:
URL:
https://www.aclweb.org/anthology/2020.emnlp-main.650
DOI:
10.18653/v1/2020.emnlp-main.650
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.emnlp-main.650.pdf
Optional supplementary material:
 2020.emnlp-main.650.OptionalSupplementaryMaterial.zip