Executive Director of the Society for Ethnomusicology (SEM)
https://www.ethnomusicology.org/news/news.asp?id=655007
Executive Director of the Society for Ethnomusicology (SEM)
https://www.ethnomusicology.org/news/news.asp?id=655007
#SEM2023 Annual Meeting Live-Streaming Schedule
"The Society for Ethnomusicology is offering live and archived video-streams of selected sessions from its 2023 Annual Meeting in Ottawa. These streams are provided as part of an effort to increase access, nationally and internationally, to the content of our meeting."
#SEM2023 registration closes *today* (09/10/2023) =>
#SEM2023 Annual Meeting has just published an *updated* preliminary programme!
El transporte urbano, protagonista en el día a día de muchas grandes ciudades, tiene consecuencias directas en la contaminación atmosférica y acústica, la congestión y las emisiones de CO2 🚗💨
#Sem2023 early-bird registration closes Friday, 15th of September (i.e. *this* Friday)
#SEM2023 Annual Meeting has published the preliminary program
=>
Two UKP papers have been accepted to *SEM 2023, which will happen July 13-14 as part of #ACL2023! Read the pre-prints here:
https://arxiv.org/abs/2211.01874
https://arxiv.org/pdf/2205.06733
Congratulations to the authors Tilman Beck, Andreas Waldis, Dominic Petrak, Nafise Sadat Moosavi and Iryna Gurevych!
Stance detection deals with identifying an author's stance towards a target. Most existing stance detection models are limited because they do not consider relevant contextual information which allows for inferring the stance correctly. Complementary context can be found in knowledge bases but integrating the context into pretrained language models is non-trivial due to the graph structure of standard knowledge bases. To overcome this, we explore an approach to integrate contextual information as text which allows for integrating contextual information from heterogeneous sources, such as structured knowledge sources and by prompting large language models. Our approach can outperform competitive baselines on a large and diverse stance detection benchmark in a cross-target setup, i.e. for targets unseen during training. We demonstrate that it is more robust to noisy context and can regularize for unwanted correlations between labels and target-specific vocabulary. Finally, it is independent of the pretrained language model in use.