no code implementations • GWC 2019 • Filip Klubička, Alfredo Maldonado, Abhijit Mahalunkar, John Kelleher
Creating word embeddings that reflect semantic relationships encoded in lexical knowledge resources is an open challenge.
no code implementations • LREC (BUCC) 2022 • Filip Klubička, Lorena Kasunić, Danijel Blazsetin, Petra Bago
The collected LRs were used for the development of neural MT engines in order to verify the quality of the resources.
no code implementations • 27 Apr 2023 • Filip Klubička, Vasudevan Nedumpozhimana, John D. Kelleher
The goal of this paper is to learn more about how idiomatic information is structurally encoded in embeddings, using a structural probing method.
no code implementations • 29 Mar 2023 • Organizers Of QueerInAI, :, Anaelia Ovalle, Arjun Subramonian, Ashwin Singh, Claas Voelcker, Danica J. Sutherland, Davide Locatelli, Eva Breznik, Filip Klubička, Hang Yuan, Hetvi J, huan zhang, Jaidev Shriram, Kruno Lehman, Luca Soldaini, Maarten Sap, Marc Peter Deisenroth, Maria Leonor Pacheco, Maria Ryskina, Martin Mundt, Milind Agarwal, Nyx McLean, Pan Xu, A Pranav, Raj Korpan, Ruchira Ray, Sarah Mathew, Sarthak Arora, ST John, Tanvi Anand, Vishakha Agrawal, William Agnew, Yanan Long, Zijie J. Wang, Zeerak Talat, Avijit Ghosh, Nathaniel Dennler, Michael Noseworthy, Sharvani Jha, Emi Baylor, Aditya Joshi, Natalia Y. Bilenko, Andrew McNamara, Raphael Gontijo-Lopes, Alex Markham, Evyn Dǒng, Jackie Kay, Manu Saraswat, Nikhil Vytla, Luke Stark
We present Queer in AI as a case study for community-led participatory design in AI.
no code implementations • 25 Jan 2023 • Filip Klubička, John D. Kelleher
Modelling taxonomic and thematic relatedness is important for building AI with comprehensive natural language understanding.
1 code implementation • 21 Oct 2022 • Filip Klubička, John D. Kelleher
Improving our understanding of how information is encoded in vector space can yield valuable interpretability insights.
no code implementations • LREC 2018 • 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.
1 code implementation • 12 May 2018 • 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.
1 code implementation • 2 Feb 2018 • 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.
1 code implementation • 14 Jun 2017 • Filip Klubička, Antonio Toral, Víctor M. Sánchez-Cartagena
We compare three approaches to statistical machine translation (pure phrase-based, factored phrase-based and neural) by performing a fine-grained manual evaluation via error annotation of the systems' outputs.