5 papers
Probing with Noise: Unpicking the Warp and Weft of Embeddings
Filip Klubička, John D. Kelleher
Improving our understanding of how information is encoded in vector space can yield valuable interpretability insights. Alongside vector dimensions, we argue that it is possible fo…
Semantic Relatedness and Taxonomic Word Embeddings
Magdalena Kacmajor, John D. Kelleher, Filip Klubicka +1
This paper connects a series of papers dealing with taxonomic word embeddings. It begins by noting that there are different types of semantic relatedness and that different lexical…
Is it worth it? Budget-related evaluation metrics for model selection
Filip Klubička, Giancarlo D. Salton, John D. Kelleher
Creating a linguistic resource is often done by using a machine learning model that filters the content that goes through to a human annotator, before going into the final resource…
Examining a hate speech corpus for hate speech detection and popularity prediction
Filip Klubička, Raquel Fernández
As research on hate speech becomes more and more relevant every day, most of it is still focused on hate speech detection. By attempting to replicate a hate speech detection experi…
Quantitative Fine-Grained Human Evaluation of Machine Translation Systems: a Case Study on English to Croatian
Filip Klubička, Antonio Toral, Víctor M. Sánchez-Cartagena
This paper presents a quantitative fine-grained manual evaluation approach to comparing the performance of different machine translation (MT) systems. We build upon the well-establ…