On 1 September 2023, the ERC Starting Grant project ExplainYourself on ‘Explainable and Robust Automatic Fact Checking’ is kicking off, and two new PhD students are joining our group.

4 papers by CopeNLU authors are accepted to appear at ACL 2023, on faithfulness of explanations, measuring intersectional biases, event extraction and few-shot stance detection.

Isabelle Augenstein was promoted to full professor, making her the youngest ever female full professor in Denmark.

2 papers by CopeNLU authors are accepted to appear at Coling 2022, on probing of question answering models.

2 papers by CopeNLU authors are accepted to appear at EMNLP 2022, on scientific document understanding.

3 papers by CopeNLU authors are accepted to appear at NAACL 2022, on hatespeech detection, misinformation detection and multilingual probing.

2 papers by CopeNLU authors are accepted to appear at AAAI 2022, on explanation generation, as well as on cross-lingual stance detection.

On 1 September 2021, the DFF Sapere Aude project EXPANSE on ‘Learning to Explain Attitudes on Social Media’ is kicking off, and four new members are joining our group.

3 papers by CopeNLU authors are accepted to appear at EMNLP 2021, on stance detection, exaggeration detection, and on counterfactually augmented data.

A paper by CopeNLU authors on multi-hop fact checking is accepted to appear at IJCAI.



Full Professor

Isabelle’s main research interests are natural language understanding, explainability and learning with limited training data.


Pepa’s research interests are multilingual fact checking and question answering.

PhD Student

Karolina’s research interests include gender-biased language detection and statistical methods.

PhD Student

Erik’s main research interests are question answering and explainability.

PhD Student

Nadav’s research interests include improving the trustworthiness and usefulness of deep models in the NLP domain.

PhD Student

Arnav’s research interests include equitable ML, mitigating online harms, and the intersection of NLP and Computational Social Science.

PhD Student

Haeun’s main research interests include enhancing explainability in fact-checking and transparency of knowledge-enhanced LM.

PhD Student

Jingyi Sun’s research interests include explainability, fact-checking, and question answering.


This is Ryan. He’s a lecturer at the University of Cambridge and a frequent collaborator of the CopeNLU group.

PhD Student

Sara’s research interests include explainable IR and NLP models, identifying biases in large text datasets, as well as working with social media data. She is a member of the DIKU ML section and IR group and co-advised by Isabelle.

PhD Intern

Marta’s research interests are explainability and fairness, with a focus on abusive language detection. She is a second-year PhD student at the University of Pisa and was visiting CopeNLU in Spring/Summer 2023.

Visiting Researcher

Kevin Roitero is an Assistant Professor (RTD/a) at the University of Udine, Italy. His research interests include Artificial Intelligence, Deep Learning, Crowdsourcing, and Information Retrieval. He was visiting CopeNLU in Spring/Summer 2023.

PhD Student

Nils researches low-resource learning and unsupervised learning as well as explainability. He successfully defended his PhD thesis in August 2023.

PhD Intern

Amelie is a PhD Student at the University of Stuttgart, interested in fact-checking, specifically for medical claims and content that people chose to share on social media. She was visiting CopeNLU in Spring 2023 to work on scientific fact checking.


Lucie’s research interests include supporting lower-resourced language communities (including Wikipedia and Wikidata) with NLP, and multilingual knowledge graphs. She was a postdoctoral researcher in the CopeNLU group from 2021-2023 and is now a postdoctoral researcher at Hasso-Plattner-Institut.


Oscar researches automating the assessment of psychological constructs using representation learning. He is an international postdoc affiliated with Lund University as well the University of Copenhagen and Stony Brook University.

PhD Student

Andreas’ main research areas are representation learning and domain adaptation, with a focus on scientific texts.

PhD Student

Dustin’s research interests include fact checking and knowledge base population, with a focus on scientific texts. He was PhD in CopeNLU from 2019 to 2023, and is now a postdoctoral researcher in the DIKU ML section.


Thea is a MSc Student in Cognitive Science at Aarhus University, visiting CopeNLU in Autumn 2022 to work on gender bias detection.


Miryam’s main research interests are syntactic parsing, multilingual NLP and interpretability. She is an assistant profesor at KU Leuven, and was previously an international postdoc affiliated with KU Leuven as well as the University of Copenhagen.

PhD Student

Nodens is interested in interpretability and bio-inspired models.

PhD Intern

Klim is a PhD Student at Ghent University. He was visiting CopeNLU in Spring 2022 to work on entity linking.


Sagnik is a postdoctoral researcher at the University of Michigan. He was a postdoctoral researcher in CopeNLU from 2020 to 2022, working on question answering and interpretability of neural black-box models.

PhD Intern

Shailza is a PhD Student at the Technical University of Kaiserslautern, and a research assistant at DFKI. She was visiting CopeNLU in Winter/Spring 2021 to work on interpretability.

PhD Student

Yova was a PhD student in the CoAStaL NLP group researching low-resource and cross-lingual learning, co-advised by Isabelle. She is now a postdoc in the same group.

PhD Student

Ana is a senior data scientist at Halfspace, Copenhagen. Prior to this, she was a PhD student in the CoAStaL NLP group from 2017-2021 co-advised by Isabelle, working on question answering.

PhD Student

Andrea’s main research interests are multilingual learning and language modelling. He was a PhD student at the University of Southern Denmark and co-advised by Isabelle.


Johannes is an associate professor at Aalborg University Copenhagen. He was a postdoc in CopeNLU from 2017 to 2020, researching multi-lingual and multi-task learning.

PhD Intern

Liesbeth is a PhD Student at KU Leuven, and was visiting CopeNLU in Spring 2020 to work on fact checking.

PhD Intern

Wei Zhao is a postdoctoral researcher at Heidelberg University, and was visiting CopeNLU in Winter 2019 to work on low-resource natural language generation.

PhD Student

Mareike was a member of the CoAStaL NLP group, researching disinformation detection and co-advised by Isabelle, and is now a researcher at DFKI.

PhD Intern

Farhad is a currently postdoc at the University of Zurich, and was visiting CopeNLU in Spring 2019 to work on domain adaptation for information extraction.

Research Intern

Zhong Xuan is a data scientist at Grab, Singapore, and was visiting CopeNLU in Summer 2019 to work on relation extraction and knowledge base population.

PhD Intern

Luna is a PhD Student at Ghent University, and was visiting CopeNLU in Spring 2019 to work on emotion detection.

PhD Intern

Giannis is a principal data scientist at Clarivate, and was visiting CopeNLU as a PhD Intern in Spring 2019 to work on joint information extraction.

Research Assistant

Now a machine learning engineer at Omhu.

Recent Publications

More Publications

Answering complex queries on incomplete knowledge graphs is a challenging task where a model needs to answer complex logical queries in …

Uncertainty approximation in text classification is an important area with applications in domain adaptation and interpretability. One …

The proliferation of harmful content on online platforms is a major societal problem, which comes in many different forms including …

Explanations of neural models aim to reveal a model’s decision-making process for its predictions. However, recent work shows …

Data-driven analyses of biases in historical texts can help illuminate the origin and development of biases prevailing in modern …

NLP methods can aid historians in analyzing textual materials in greater volumes than manually feasible. Developing such methods poses …

The task of Stance Detection is concerned with identifying the attitudes expressed by an author towards a target of interest. This task …

Pre-trained language models have been known to perpetuate biases from the underlying datasets to downstream tasks. However, these …

Language embeds information about social, cultural, and political values people hold. Prior work has explored social and potentially …

Dual use, the intentional, harmful reuse of technology and scientific artefacts, is a problem yet to be well-defined within the context …

Recent Posts

We gave a tutorial including lab session at the first ALPS (Advanced Language Prcoessing School. If you want to learn more about …

Yes, we all have planned to be under palm trees of Punta Cana now and to sip drinks with umbrellas. Let’s make a new plan: an …

The University of Copenhagen is a great place if you’re both interested in high-quality NLP research and a high quality of life.



Learning with Limited Labelled Data

Learning with limited labelled data, including multi-task learning, weakly supervised and zero-shot learning

Stance Detection and Fact Checking

Determine the attitude expressed in a text towards a topic, and use this for automatic evidence-based fact checking

Explainable Machine Learning

Explaining relationships between inputs and outputs of black-box machine learning models

Gender Bias Detection

Automatically detecting gendered language, and to what degree attitudes towards entities are influenced by gender bias

Multilingual Learning

Training models to work well for multiple languages, including low-resource ones

Scholarly Data Processing

Automatically processing scholarly data to assist researchers in finding publications, writing better papers, or tracking their impact.

Question Answering

Answering questions automatically, including in conversational settings

Knowledge Base Population

Extract information about entities, phrases and relations between them from text to populate knowledge bases