468 citations · 517 across the 13 of their papers we have counts for
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cs.CL2021
Benchmarking down-scaled (not so large) pre-trained language models
M. Aßenmacher, P. Schulze, C. Heumann
Large Transformer-based language models are pre-trained on corpora of varying sizes, for a different number of steps and with different batch sizes. At the same time, more fundamen…
cs.CL2021★ 4 cited
A Bayesian approach to modeling topic-metadata relationships
P. Schulze, S. Wiegrebe, P. W. Thurner +2
The objective of advanced topic modeling is not only to explore latent topical structures, but also to estimate relationships between the discovered topics and theoretically releva…
cs.CL2021
Re-Evaluating GermEval17 Using German Pre-Trained Language Models
M. Aßenmacher, A. Corvonato, C. Heumann
The lack of a commonly used benchmark data set (collection) such as (Super-)GLUE (Wang et al., 2018, 2019) for the evaluation of non-English pre-trained language models is a severe…