4 citations · 4 across the 1 of their papers we have counts for
6 papers
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…
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…
Low Resource Multi-Task Sequence Tagging -- Revisiting Dynamic Conditional Random Fields
Jonas Pfeiffer, Edwin Simpson, Iryna Gurevych
We compare different models for low resource multi-task sequence tagging that leverage dependencies between label sequences for different tasks. Our analysis is aimed at datasets w…
MAD-X: An Adapter-Based Framework for Multi-Task Cross-Lingual Transfer
Jonas Pfeiffer, Ivan Vulić, Iryna Gurevych +1
The main goal behind state-of-the-art pre-trained multilingual models such as multilingual BERT and XLM-R is enabling and bootstrapping NLP applications in low-resource languages t…
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…
FAMULUS: Interactive Annotation and Feedback Generation for Teaching Diagnostic Reasoning
Jonas Pfeiffer, Christian M. Meyer, Claudia Schulz +7
Our proposed system FAMULUS helps students learn to diagnose based on automatic feedback in virtual patient simulations, and it supports instructors in labeling training data. Diag…