SQuARE: Towards Multi-Domain and Few-Shot Collaborating Question Answering Agents


The exponential growth of Question Answering (QA) models and datasets is opening new application and research possibilities, such as modular and compositional systems. In this talk, we will present how to achieve multi-domain QA systems through the collaboration of multiple models. We will further explore how relevance feedback can be used for few-shot document re-ranking, and will finish by introducing UKP-SQuARE, the first online platform that provides an ecosystem for QA research. With UKP-SQuARE, users can deploy, run, analyze, and compare models with a standardized interface from multiple perspectives, such as general behavior, explainability, adversarial attacks, and behavioral tests, enabling a holistic analysis.


Iryna Gurevych (PhD 2003, U. Duisburg-Essen, Germany) is professor of Computer Science and director of the Ubiquitous Knowledge Processing (UKP) Lab at the Technical University (TU) of Darmstadt in Germany. Her main research interests are in machine learning for large-scale language understanding and text semantics. Iryna’s work has received numerous awards. Examples are the ACL fellow award 2020 and the first Hessian LOEWE Distinguished Chair award (2,5 mil. Euro) in 2021. Iryna is co-director of the NLP program within ELLIS, a European network of excellence in machine learning. She is currently the president of the Association of Computational Linguistics. In 2022, she received an ERC Advanced Grant to support her vision for the next big step in NLP “InterText – Modeling Text as a Living Object in a Cross-Document Context”.

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