Findings of WASSA 2024 Shared Task on Empathy and Personality Detection in Interactions
Findings of the shared task on Empathy, Personality, and Emotion Detection from the WASSA workshop @ ACL.
Jul 1, 2024
Findings of WASSA 2023 Shared Task on Empathy, Emotion and Personality Detection in Conversation and Reactions to News Articles
Findings of the shared task on Empathy, Personality, and Emotion Detection from the WASSA workshop @ ACL.
Jul 1, 2023
Multilingual Multi-Target Stance Recognition in Online Public Consultations
We've developed a machine learning approach to automatically recognize the opinions of citizens in online public consultations, using three datasets to train a model that can classify stances on various topics. Our work experiments with different methods, including self-supervised learning, and makes several annotated datasets available for others to use. This work was used in the Touché shared task of CLEF 2023.
Apr 1, 2023
CoFE: A New Dataset of Intra-Multilingual Multi-target Stance Classification from an Online European Participatory Democracy Platform
A new dataset for Stance Recognition using data from the Participatory Democracy platform of the Conference for the Future of Europe. This dataset contains highly-multilingual interactions, as the platform used Machine Translation, in the sense that users interacts in using their (different) native languages in the same thread.
Nov 1, 2022
Opinions in Interactions : New Annotations of the SEMAINE Database
We've added new opinion annotations to the SEMAINE dataset, which captures dyadic interactions between humans and virtual agents, resulting in a rich dataset with over 73,000 words and 6 hours of conversation. Our annotations and proposed baseline model using RoBERTa embeddings achieve promising results, with a F1-score of 0.72, making it a valuable resource for opinion detection in human-computer interactions.
Jun 1, 2022
Debating Europe: A Multilingual Multi-Target Stance Classification Dataset of Online Debates
A new dataset of 2,600 online debate comments has been created to improve stance classification models. Fine-tuning and semi-supervised learning can boost accuracy by 3.4% over a baseline model.
Jun 1, 2022