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.

2 papers by CopeNLU authors are accepted to appear at ACL 2021, on interpretability, as well as on scientific document understanding.

A paper by CopeNLU authors on typological blinding of cross-lingual models is accepted to appear at EACL.

2 PhD fellowships and 2 postdoctoral positions on explainable stance detection are available in CopeNLU. The positions are funded by a DFF Sapere Aude research leader fellowship.

7 CopeNLU papers are accepted to appear at EMNLP 2020, on fact checking, explainability, domain adaptation, and more.

2 papers by CopeNLU authors are accepted to appear at ACL 2020, on explainable fact checking, as as well as on script conversion.

4 papers by CopeNLU authors are to be presented at EMNLP 2019 and co-located events, on fact checking and disinformation, as well as on multi-task and multi-lingual learning.

2 papers by CopeNLU authors are accepted to appear at ACL 2019, on discovering probabilistic implications in typological knowledge bases as well as gendered language



Associate Professor

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

PhD Student

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

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.

PhD Student

Nils researches low-resource learning and unsupervised learning as well as explainability.

PhD Student

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


Sagnik is interested in question answering and interpretability of neural black-box models.

PhD Student

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


Lucie’s research interests include supporting lower-resourced language communities (including Wikipedia and Wikidata) with NLP, and multilingual knowledge graphs.

PhD Student

Nodens is interested in natural language understanding, explainability, and bio-inspired models.

PhD Student

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


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


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.


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

PhD Intern

Shailza is a PhD Student at the Technical University of Kaiserslautern, and a research assistant at DFKI. She is 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 postdoc in the CoAStaL NLP group. Prior to this, she was a PhD student in the same 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 PhD Student at TU Darmstadt, 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 postdoc in the same group.

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 student at Yale-NUS College, 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 postdoc at Vrije Unversiteit Brussel, 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

Explanations shed light on a machine learning model’s rationales and can aid in identifying deficiencies in its reasoning …

The goal of stance detection is to determine the viewpoint expressed in a piece of text towards a target. These viewpoints or contexts …

Truth can vary over time. Therefore, fact-checking decisions on claim veracity should take into account temporal information of both …

Stance detection concerns the classification of a writer’s viewpoint towards a target. There are different task variants, e.g., …

As NLP models are increasingly deployed in socially situated settings such as online abusive content detection, ensuring these models …

Public trust in science depends on honest and factual communication of scientific papers. However, recent studies have demonstrated a …

Alongside huge volumes of research on deep learning models in NLP in the recent years, there has been also much work on benchmark …

Emotion lexica are commonly used resources to combat data poverty in automatic emotion detection. However, methodological issues emerge …

Most work on scholarly document processing assumes that the information processed is trust-worthy and factually correct. However, this …

Cross-lingual representations have the potential to make NLP techniques available to the vast majority of languages in the world. …

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

Multilingual Learning

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

Question Answering

Answering questions automatically, including in conversational settings

Gender Bias Detection

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

Scholarly Data Processing

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

Knowledge Base Population

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