85 citations · 139 across the 6 of their papers we have counts for
5 papers · 1 filter
Improving QA Generalization by Concurrent Modeling of Multiple Biases
Mingzhu Wu, Nafise Sadat Moosavi, Andreas Rücklé +1
Existing NLP datasets contain various biases that models can easily exploit to achieve high performances on the corresponding evaluation sets. However, focusing on dataset-specific…
MultiCQA: Zero-Shot Transfer of Self-Supervised Text Matching Models on a Massive Scale
Andreas Rücklé, Jonas Pfeiffer, Iryna Gurevych
We study the zero-shot transfer capabilities of text matching models on a massive scale, by self-supervised training on 140 source domains from community question answering forums…
AdapterDrop: On the Efficiency of Adapters in Transformers
Andreas Rücklé, Gregor Geigle, Max Glockner +4
Massively pre-trained transformer models are computationally expensive to fine-tune, slow for inference, and have large storage requirements. Recent approaches tackle these shortco…
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…