25 citations · 40 across the 6 of their papers we have counts for
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cs.CL2021★ 25 cited
NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation
Kaustubh D. Dhole, Varun Gangal, Sebastian Gehrmann +122
Data augmentation is an important component in the robustness evaluation of models in natural language processing (NLP) and in enhancing the diversity of the data they are trained…
cs.CL2021
Robustifying Multi-hop QA through Pseudo-Evidentiality Training
Kyungjae Lee, Seung-won Hwang, Sang-eun Han +1
This paper studies the bias problem of multi-hop question answering models, of answering correctly without correct reasoning. One way to robustify these models is by supervising to…