70 citations · 145 across the 19 of their papers we have counts for
4 papers · 1 filter
Exploring Predictive Uncertainty and Calibration in NLP: A Study on the Impact of Method & Data Scarcity
Dennis Ulmer, Jes Frellsen, Christian Hardmeier
We investigate the problem of determining the predictive confidence (or, conversely, uncertainty) of a neural classifier through the lens of low-resource languages. By training mod…
State-of-the-art generalisation research in NLP: A taxonomy and review
Dieuwke Hupkes, Mario Giulianelli, Verna Dankers +17
The ability to generalise well is one of the primary desiderata of natural language processing (NLP). Yet, what 'good generalisation' entails and how it should be evaluated is not…
deep-significance - Easy and Meaningful Statistical Significance Testing in the Age of Neural Networks
Dennis Ulmer, Christian Hardmeier, Jes Frellsen
A lot of Machine Learning (ML) and Deep Learning (DL) research is of an empirical nature. Nevertheless, statistical significance testing (SST) is still not widely used. This endang…
Experimental Standards for Deep Learning in Natural Language Processing Research
Dennis Ulmer, Elisa Bassignana, Max Müller-Eberstein +5
The field of Deep Learning (DL) has undergone explosive growth during the last decade, with a substantial impact on Natural Language Processing (NLP) as well. Yet, compared to more…