TY - GEN
T1 - LongEval
T2 - 46th European Conference on Information Retrieval, ECIR 2024
AU - Alkhalifa, Rabab
AU - Borkakoty, Hsuvas
AU - Deveaud, Romain
AU - El-Ebshihy, Alaa
AU - Espinosa-Anke, Luis
AU - Fink, Tobias
AU - Gonzalez-Saez, Gabriela
AU - Galuščáková, Petra
AU - Goeuriot, Lorraine
AU - Iommi, David
AU - Liakata, Maria
AU - Madabushi, Harish Tayyar
AU - Medina-Alias, Pablo
AU - Mulhem, Philippe
AU - Piroi, Florina
AU - Popel, Martin
AU - Servan, Christophe
AU - Zubiaga, Arkaitz
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
PY - 2024
Y1 - 2024
N2 - This paper introduces the planned second LongEval Lab, part of the CLEF 2024 conference. The aim of the lab’s two tasks is to give researchers test data for addressing temporal effectiveness persistence challenges in both information retrieval and text classification, motivated by the fact that model performance degrades as the test data becomes temporally distant from the training data. LongEval distinguishes itself from traditional IR and classification tasks by emphasizing the evaluation of models designed to mitigate performance drop over time using evolving data. The second LongEval edition will further engage the IR community and NLP researchers in addressing the crucial challenge of temporal persistence in models, exploring the factors that enable or hinder it, and identifying potential solutions along with their limitations.
AB - This paper introduces the planned second LongEval Lab, part of the CLEF 2024 conference. The aim of the lab’s two tasks is to give researchers test data for addressing temporal effectiveness persistence challenges in both information retrieval and text classification, motivated by the fact that model performance degrades as the test data becomes temporally distant from the training data. LongEval distinguishes itself from traditional IR and classification tasks by emphasizing the evaluation of models designed to mitigate performance drop over time using evolving data. The second LongEval edition will further engage the IR community and NLP researchers in addressing the crucial challenge of temporal persistence in models, exploring the factors that enable or hinder it, and identifying potential solutions along with their limitations.
KW - Evaluation
KW - Information Retrieval
KW - Temporal Generalisability
KW - Temporal Persistence
KW - Text Classification
UR - https://www.scopus.com/pages/publications/85189363858
U2 - 10.1007/978-3-031-56072-9_8
DO - 10.1007/978-3-031-56072-9_8
M3 - Conference contribution
AN - SCOPUS:85189363858
SN - 9783031560712
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 60
EP - 66
BT - Advances in Information Retrieval - 46th European Conference on Information Retrieval, ECIR 2024, Proceedings
A2 - Goharian, Nazli
A2 - Tonellotto, Nicola
A2 - He, Yulan
A2 - Lipani, Aldo
A2 - McDonald, Graham
A2 - Macdonald, Craig
A2 - Ounis, Iadh
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 24 March 2024 through 28 March 2024
ER -