201 citations · 1k across the 85 of their papers we have counts for
11 papers · 1 filter
Aggregating Soft Labels from Crowd Annotations Improves Uncertainty Estimation Under Distribution Shift
Dustin Wright, Isabelle Augenstein
Selecting an effective training signal for machine learning tasks is difficult: expert annotations are expensive, and crowd-sourced annotations may not be reliable. Recent work has…
Modeling Information Change in Science Communication with Semantically Matched Paraphrases
Dustin Wright, Jiaxin Pei, David Jurgens +1
Whether the media faithfully communicate scientific information has long been a core issue to the science community. Automatically identifying paraphrased scientific findings could…
Revisiting Softmax for Uncertainty Approximation in Text Classification
Andreas Nugaard Holm, Dustin Wright, Isabelle Augenstein
Uncertainty approximation in text classification is an important area with applications in domain adaptation and interpretability. One of the most widely used uncertainty approxima…
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…
Counterfactually Augmented Data and Unintended Bias: The Case of Sexism and Hate Speech Detection
Indira Sen, Mattia Samory, Claudia Wagner +1
Counterfactually Augmented Data (CAD) aims to improve out-of-domain generalizability, an indicator of model robustness. The improvement is credited with promoting core features of…
Same Neurons, Different Languages: Probing Morphosyntax in Multilingual Pre-trained Models
Karolina Stańczak, Edoardo Ponti, Lucas Torroba Hennigen +2
The success of multilingual pre-trained models is underpinned by their ability to learn representations shared by multiple languages even in absence of any explicit supervision. Ho…
Fact Checking with Insufficient Evidence
Pepa Atanasova, Jakob Grue Simonsen, Christina Lioma +1
Automating the fact checking (FC) process relies on information obtained from external sources. In this work, we posit that it is crucial for FC models to make veracity predictions…
Generating Scientific Claims for Zero-Shot Scientific Fact Checking
Dustin Wright, David Wadden, Kyle Lo +4
Automated scientific fact checking is difficult due to the complexity of scientific language and a lack of significant amounts of training data, as annotation requires domain exper…
Probing Pre-Trained Language Models for Cross-Cultural Differences in Values
Arnav Arora, Lucie-Aimée Kaffee, Isabelle Augenstein
Language embeds information about social, cultural, and political values people hold. Prior work has explored social and potentially harmful biases encoded in Pre-Trained Language…
Neighborhood Contrastive Learning for Scientific Document Representations with Citation Embeddings
Malte Ostendorff, Nils Rethmeier, Isabelle Augenstein +2
Learning scientific document representations can be substantially improved through contrastive learning objectives, where the challenge lies in creating positive and negative train…
A Latent-Variable Model for Intrinsic Probing
Karolina Stańczak, Lucas Torroba Hennigen, Adina Williams +2
The success of pre-trained contextualized representations has prompted researchers to analyze them for the presence of linguistic information. Indeed, it is natural to assume that…