13 citations · 13 across the 1 of their papers we have counts for
4 papers · 1 filter
AdapterHub: A Framework for Adapting Transformers
Jonas Pfeiffer, Andreas Rücklé, Clifton Poth +5
The current modus operandi in NLP involves downloading and fine-tuning pre-trained models consisting of millions or billions of parameters. Storing and sharing such large trained m…
AdapterFusion: Non-Destructive Task Composition for Transfer Learning
Jonas Pfeiffer, Aishwarya Kamath, Andreas Rücklé +2
Sequential fine-tuning and multi-task learning are methods aiming to incorporate knowledge from multiple tasks; however, they suffer from catastrophic forgetting and difficulties i…
What do Deep Networks Like to Read?
Jonas Pfeiffer, Aishwarya Kamath, Iryna Gurevych +1
Recent research towards understanding neural networks probes models in a top-down manner, but is only able to identify model tendencies that are known a priori. We propose Suscepti…
A Survey on Semantic Parsing
Aishwarya Kamath, Rajarshi Das
A significant amount of information in today's world is stored in structured and semi-structured knowledge bases. Efficient and simple methods to query them are essential and must…