1

For different reasons, text can be difficult to read and understand for many people, especially if the text’s language is too complex. …

The human-centric explainable artificial intelligence (HCXAI) community has raised the need for framing the explanation process as a …

To cope with the COVID-19 pandemic, many jurisdictions have introduced new or altered existing legislation. Even though these new rules …

Document embeddings and similarity measures underpin content-based recommender systems, whereby a document is commonly represented as a …

Transformer-based language models usually treat texts as linear sequences. However, most texts also have an inherent hierarchical …

The quality of machine-generated text is a complex construct consisting of various aspects and dimensions. We present a study that aims …

Pre-trained language models (PLM) are effective components of few-shot named entity recognition (NER) approaches when augmented with …

Relation classification models are conventionally evaluated using only a single measure, e.g., micro-F1, macro-F1 or AUC. In this work, …

Amid a discussion about Green AI in which we see explainability neglected, we explore the possibility to efficiently approximate …

In the language domain, as in other domains, neural explainability takes an ever more important role, with feature attribution methods …