201 citations · 1k across the 85 of their papers we have counts for
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A Survey on Gender Bias in Natural Language Processing
Karolina Stanczak, Isabelle Augenstein
Language can be used as a means of reproducing and enforcing harmful stereotypes and biases and has been analysed as such in numerous research. In this paper, we present a survey o…
Generating Fluent Fact Checking Explanations with Unsupervised Post-Editing
Shailza Jolly, Pepa Atanasova, Isabelle Augenstein
Fact-checking systems have become important tools to verify fake and misguiding news. These systems become more trustworthy when human-readable explanations accompany the veracity…
Quantifying Gender Biases Towards Politicians on Reddit
Sara Marjanovic, Karolina Stańczak, Isabelle Augenstein
Despite attempts to increase gender parity in politics, global efforts have struggled to ensure equal female representation. This is likely tied to implicit gender biases against w…
Diagnostics-Guided Explanation Generation
Pepa Atanasova, Jakob Grue Simonsen, Christina Lioma +1
Explanations shed light on a machine learning model's rationales and can aid in identifying deficiencies in its reasoning process. Explanation generation models are typically train…
Semi-Supervised Exaggeration Detection of Health Science Press Releases
Dustin Wright, Isabelle Augenstein
Public trust in science depends on honest and factual communication of scientific papers. However, recent studies have demonstrated a tendency of news media to misrepresent scienti…
Towards Explainable Fact Checking
Isabelle Augenstein
The past decade has seen a substantial rise in the amount of mis- and disinformation online, from targeted disinformation campaigns to influence politics, to the unintentional spre…
QA Dataset Explosion: A Taxonomy of NLP Resources for Question Answering and Reading Comprehension
Anna Rogers, Matt Gardner, Isabelle Augenstein
Alongside huge volumes of research on deep learning models in NLP in the recent years, there has been also much work on benchmark datasets needed to track modeling progress. Questi…
Is Sparse Attention more Interpretable?
Clara Meister, Stefan Lazov, Isabelle Augenstein +1
Sparse attention has been claimed to increase model interpretability under the assumption that it highlights influential inputs. Yet the attention distribution is typically over re…
Determining the Credibility of Science Communication
Isabelle Augenstein
Most work on scholarly document processing assumes that the information processed is trustworthy and factually correct. However, this is not always the case. There are two core cha…
CiteWorth: Cite-Worthiness Detection for Improved Scientific Document Understanding
Dustin Wright, Isabelle Augenstein
Scientific document understanding is challenging as the data is highly domain specific and diverse. However, datasets for tasks with scientific text require expensive manual annota…
Quantifying Gender Bias Towards Politicians in Cross-Lingual Language Models
Karolina Stańczak, Sagnik Ray Choudhury, Tiago Pimentel +2
Recent research has demonstrated that large pre-trained language models reflect societal biases expressed in natural language. The present paper introduces a simple method for prob…
Cross-Domain Label-Adaptive Stance Detection
Momchil Hardalov, Arnav Arora, Preslav Nakov +1
Stance detection concerns the classification of a writer's viewpoint towards a target. There are different task variants, e.g., stance of a tweet vs. a full article, or stance with…
University of Copenhagen Participation in TREC Health Misinformation Track 2020
Lucas Chaves Lima, Dustin Brandon Wright, Isabelle Augenstein +1
In this paper, we describe our participation in the TREC Health Misinformation Track 2020. We submitted runs to the Total Recall Task and 13 runs to the Ad Hoc task. Our appro…
A Neighbourhood Framework for Resource-Lean Content Flagging
Sheikh Muhammad Sarwar, Dimitrina Zlatkova, Momchil Hardalov +3
We propose a novel framework for cross-lingual content flagging with limited target-language data, which significantly outperforms prior work in terms of predictive performance. Th…
A Survey on Stance Detection for Mis- and Disinformation Identification
Momchil Hardalov, Arnav Arora, Preslav Nakov +1
Understanding attitudes expressed in texts, also known as stance detection, plays an important role in systems for detecting false information online, be it misinformation (uninten…
Detecting Harmful Content On Online Platforms: What Platforms Need Vs. Where Research Efforts Go
Arnav Arora, Preslav Nakov, Momchil Hardalov +8
The proliferation of harmful content on online platforms is a major societal problem, which comes in many different forms including hate speech, offensive language, bullying and ha…
A Primer on Contrastive Pretraining in Language Processing: Methods, Lessons Learned and Perspectives
Nils Rethmeier, Isabelle Augenstein
Modern natural language processing (NLP) methods employ self-supervised pretraining objectives such as masked language modeling to boost the performance of various application task…
Disembodied Machine Learning: On the Illusion of Objectivity in NLP
Zeerak Waseem, Smarika Lulz, Joachim Bingel +1
Machine Learning seeks to identify and encode bodies of knowledge within provided datasets. However, data encodes subjective content, which determines the possible outcomes of the…
Does Typological Blinding Impede Cross-Lingual Sharing?
Johannes Bjerva, Isabelle Augenstein
Bridging the performance gap between high- and low-resource languages has been the focus of much previous work. Typological features from databases such as the World Atlas of Langu…