1 code implementation • 16 Oct 2023 • Sagi Shaier, Kevin Bennett, Lawrence Hunter, Katharina von der Wense
State-of-the-art question answering (QA) models exhibit a variety of social biases (e. g., with respect to sex or race), generally explained by similar issues in their training data.
no code implementations • 9 Jul 2023 • Hector Zenil, Jesper Tegnér, Felipe S. Abrahão, Alexander Lavin, Vipin Kumar, Jeremy G. Frey, Adrian Weller, Larisa Soldatova, Alan R. Bundy, Nicholas R. Jennings, Koichi Takahashi, Lawrence Hunter, Saso Dzeroski, Andrew Briggs, Frederick D. Gregory, Carla P. Gomes, Jon Rowe, James Evans, Hiroaki Kitano, Ross King
Yet, AI has contributed less to fundamental science in part because large data sets of high-quality data for scientific practice and model discovery are more difficult to access.
no code implementations • 19 Dec 2022 • Sagi Shaier, Lawrence Hunter, Katharina Kann
Many dialogue systems (DSs) lack characteristics humans have, such as emotion perception, factuality, and informativeness.
no code implementations • WS 2019 • William Baumgartner, Michael Bada, Sampo Pyysalo, Manuel R. Ciosici, Negacy Hailu, Harrison Pielke-Lombardo, Michael Regan, Lawrence Hunter
As part of the BioNLP Open Shared Tasks 2019, the CRAFT Shared Tasks 2019 provides a platform to gauge the state of the art for three fundamental language processing tasks {---} dependency parse construction, coreference resolution, and ontology concept identification {---} over full-text biomedical articles.