activity
20182022
most citedMachine Reading, Fast and Slow: When Do Models "Understand" Language?

4 citations · 6 across the 4 of their papers we have counts for

collaborators

12 papers

cs.CL20224 cited

Machine Reading, Fast and Slow: When Do Models "Understand" Language?

Sagnik Ray Choudhury, Anna Rogers, Isabelle Augenstein

Two of the most fundamental challenges in Natural Language Understanding (NLU) at present are: (a) how to establish whether deep learning-based models score highly on NLU benchmark…

cs.CL2022

What Factors Should Paper-Reviewer Assignments Rely On? Community Perspectives on Issues and Ideals in Conference Peer-Review

Terne Sasha Thorn Jakobsen, Anna Rogers

Both scientific progress and individual researcher careers depend on the quality of peer review, which in turn depends on paper-reviewer matching. Surprisingly, this problem has be…

cs.CL2021

Generalization in NLI: Ways (Not) To Go Beyond Simple Heuristics

Prajjwal Bhargava, Aleksandr Drozd, Anna Rogers

Much of recent progress in NLU was shown to be due to models' learning dataset-specific heuristics. We conduct a case study of generalization in NLI (from MNLI to the adversarially…

cs.CL20212 cited

Just What do You Think You're Doing, Dave?' A Checklist for Responsible Data Use in NLP

Anna Rogers, Tim Baldwin, Kobi Leins

A key part of the NLP ethics movement is responsible use of data, but exactly what that means or how it can be best achieved remain unclear. This position paper discusses the core…

cs.CL2021

On the Interaction of Belief Bias and Explanations

Ana Valeria Gonzalez, Anna Rogers, Anders Søgaard

A myriad of explainability methods have been proposed in recent years, but there is little consensus on how to evaluate them. While automatic metrics allow for quick benchmarking,…

cs.CL2021

BERT Busters: Outlier Dimensions that Disrupt Transformers

Olga Kovaleva, Saurabh Kulshreshtha, Anna Rogers +1

Multiple studies have shown that Transformers are remarkably robust to pruning. Contrary to this received wisdom, we demonstrate that pre-trained Transformer encoders are surprisin…