Natural Language Processing (NLP)

NLP is a subfield of artificial intelligence, concerned with automatically processing as well as understanding text, typically using statistical models and machine learning. The resulting models can be used for applications such as correcting grammatical errors, summarising documents, answering questions or translating texts.

In our research group, we are interested in traditional core as well as emerging topics in natural language processing. We focus on researching models that require little direct input from humans to work well, and models that work well across languages. We are further interested in exploiting different modalities, for instance visual and textual data referring to the same event from captioned images, or speech and image sequences from videos.

Our work has been reported on in several international newspapers, nominated for and awarded with several best paper awards at scientific conferences, and established Copenhagen as an international center for NLP. Since its inception in 2013, the group grown to three faculty members, and is now ranked in the top 10 of NLP groups worldwide and 2nd in Europe, according to CSRankings.



Anders Søgaard conducts research in core topics in natural language processing and applied machine learning, including (cross-lingual and cross-domain) transfer- and multi-task learning, adversarial learning, and reinforcement learning.

Isabelle Augenstein’s main research interests are natural language understanding and learning with limited labelled data. This includes emerging topics such as stance detection and fact checking; as well as representation, few-shot and weakly supervised learning.

Desmond Elliott’s research focuses on models that solve problems by integrating vision and language. He is interested in multimodal machine translation, image captioning, and multilingual video understanding.




Learning Multilingual Natural Language Understanding from Transfer & Reinforcement (2018-2022)
Google Focused Research Award
Led by Anders Søgaard

Building Emotionally Intelligent Dialogue Systems (2018-2021)
BotXO Research Award
Led by Anders Søgaard

Automated Writing Assistant with Artificial Intelligence (2018-2021)
Industrial PhD Grant
Led by Anders Søgaard

Advanced Machine Learning for Automated Omni-Channel Support (2018-2020)
Grand Solutions grant from Innovation Fund Denmark
Led by Stephen Alstrup
More information (in Danish) about Advanced Machine Learning for Automated Omni-Channel Support

NLPL: Nordic Language Processing Laboratory (2017-19)
Infrastructure grant from NeIC
Led by Bjørn Lindi
More information about NLPL

Paper-based paper search (2018)
AI2 Faculty Award
Led by Anders Søgaard

Digital Disinformation (2016-18)
Research grant from the Carlsberg Foundation
Led by Rebecca Adler-Nissen

ReProsis: Real Time Big Data Product Analysis – Product Management System in International Markets (2016-18)
Research project funded by Eurostars

Interactive text simplification for dyslexics (2015-18)
Research project supported by Trygfonden