2 PhD fellowships and 2 postdoctoral positions on explainable fact checking are available in CopeNLU. The positions are funded by the ERC Starting Grant project ExplainYourself.

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

PhD fellowship on Explainable Machine Learning available. The successfull candidate will be supervised by Christina Lioma and Isabelle Augenstein.

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.

People

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Full Professor

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

Postdoc

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.

PhD Student

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

Postdoc

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

PhD Student

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

Intern

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

PhD Student

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

Lecturer

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

Postdoc

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.

Postdoc

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.

Postdoc

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.

Postdoc

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

The success of pre-trained contextualized representations has prompted researchers to analyze them for the presence of linguistic …

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

Fact-checking systems have become important tools to verify fake and misguiding news. These systems become more trustworthy when …

Whether the media faithfully communicate scientific information has long been a core issue to the science community. Automatically …

Learning scientific document representations can be substantially improved through contrastive learning objectives, where the challenge …

In our continuously evolving world, entities change over time and new, previously non-existing or unknown, entities appear. We study …

There have been many efforts to try to understand what grammatical knowledge (e.g., ability to understand the part of speech of a …

Two of the most fundamental challenges in Natural Language Understanding (NLU) at present are: (a) how to establish whether deep …

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

Modern natural language processing (NLP) methods employ self-supervised pretraining objectives such as masked language modeling to …

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.

Projects

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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

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