39 citations · 56 across the 10 of their papers we have counts for
17 papers · 1 filter
PERFECT: Prompt-free and Efficient Few-shot Learning with Language Models
Rabeeh Karimi Mahabadi, Luke Zettlemoyer, James Henderson +4
Current methods for few-shot fine-tuning of pretrained masked language models (PLMs) require carefully engineered prompts and verbalizers for each new task to convert examples into…
Graph Refinement for Coreference Resolution
Lesly Miculicich, James Henderson
The state-of-the-art models for coreference resolution are based on independent mention pair-wise decisions. We propose a modelling approach that learns coreference at the document…
Variational Information Bottleneck for Effective Low-Resource Fine-Tuning
Rabeeh Karimi Mahabadi, Yonatan Belinkov, James Henderson
While large-scale pretrained language models have obtained impressive results when fine-tuned on a wide variety of tasks, they still often suffer from overfitting in low-resource s…
Parameter-efficient Multi-task Fine-tuning for Transformers via Shared Hypernetworks
Rabeeh Karimi Mahabadi, Sebastian Ruder, Mostafa Dehghani +1
State-of-the-art parameter-efficient fine-tuning methods rely on introducing adapter modules between the layers of a pretrained language model. However, such modules are trained se…
The Unstoppable Rise of Computational Linguistics in Deep Learning
James Henderson
In this paper, we trace the history of neural networks applied to natural language understanding tasks, and identify key contributions which the nature of language has made to the…
End-to-End Bias Mitigation by Modelling Biases in Corpora
Rabeeh Karimi Mahabadi, Yonatan Belinkov, James Henderson
Several recent studies have shown that strong natural language understanding (NLU) models are prone to relying on unwanted dataset biases without learning the underlying task, resu…