2 citations · 2 across the 1 of their papers we have counts for
3 papers
cs.CL2021★ 2 cited
A Primer on Contrastive Pretraining in Language Processing: Methods, Lessons Learned and Perspectives
Nils Rethmeier, Isabelle Augenstein
Modern natural language processing (NLP) methods employ self-supervised pretraining objectives such as masked language modeling to boost the performance of various application task…
cs.CL2020
Data-Efficient Pretraining via Contrastive Self-Supervision
Nils Rethmeier, Isabelle Augenstein
For natural language processing `text-to-text' tasks, the prevailing approaches heavily rely on pretraining large self-supervised models on increasingly larger `task-external' data…
cs.LG2019
TX-Ray: Quantifying and Explaining Model-Knowledge Transfer in (Un-)Supervised NLP
Nils Rethmeier, Vageesh Kumar Saxena, Isabelle Augenstein
While state-of-the-art NLP explainability (XAI) methods focus on explaining per-sample decisions in supervised end or probing tasks, this is insufficient to explain and quantify mo…