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Investigating the Impact of Model Instability on Explanations and Uncertainty
Sara Vera Marjanović, Isabelle Augenstein, Christina Lioma
Explainable AI methods facilitate the understanding of model behaviour, yet, small, imperceptible perturbations to inputs can vastly distort explanations. As these explanations are…
Why Should This Article Be Deleted? Transparent Stance Detection in Multilingual Wikipedia Editor Discussions
Lucie-Aimée Kaffee, Arnav Arora, Isabelle Augenstein
The moderation of content on online platforms is usually non-transparent. On Wikipedia, however, this discussion is carried out publicly and the editors are encouraged to use the c…
Adapting Neural Link Predictors for Data-Efficient Complex Query Answering
Erik Arakelyan, Pasquale Minervini, Daniel Daza +2
Answering complex queries on incomplete knowledge graphs is a challenging task where a model needs to answer complex logical queries in the presence of missing knowledge. Prior wor…
Revisiting Softmax for Uncertainty Approximation in Text Classification
Andreas Nugaard Holm, Dustin Wright, Isabelle Augenstein
Uncertainty approximation in text classification is an important area with applications in domain adaptation and interpretability. One of the most widely used uncertainty approxima…
Diagnostics-Guided Explanation Generation
Pepa Atanasova, Jakob Grue Simonsen, Christina Lioma +1
Explanations shed light on a machine learning model's rationales and can aid in identifying deficiencies in its reasoning process. Explanation generation models are typically train…
Transformer Based Multi-Source Domain Adaptation
Dustin Wright, Isabelle Augenstein
In practical machine learning settings, the data on which a model must make predictions often come from a different distribution than the data it was trained on. Here, we investiga…