TY - CONF
T1 - A taxonomy and review of generalization research in NLP
AU - Hupkes, Dieuwke
AU - Giulianelli, Mario
AU - Dankers, Verna
AU - Artetxe, Mikel
AU - Elazar, Yanai
AU - Pimentel, Tiago
AU - Christodoulopoulos, Christos
AU - Lasri, Karim
AU - Saphra, Naomi
AU - Sinclair, Arabella
AU - Ulmer, Dennis Thomas
AU - Schottmann, Florian
AU - Batsuren, Khuyagbaatar
AU - Sun, Kaiser
AU - Sinha, Koustouv
AU - Khalatbari, Leila
AU - Ryskina, Maria
AU - Cotterell, Ryan
AU - Jin, Zhijing
PY - 2023/10/1
Y1 - 2023/10/1
N2 - The ability to generalize well is one of the primary desiderata for models of natural language processing (NLP), but what ‘good generalization’ entails and how it should be evaluated is not well understood. In this Analysis we present a taxonomy for characterizing and understanding generalization research in NLP. The proposed taxonomy is based on an extensive literature review and contains five axes along which generalization studies can differ: their main motivation, the type of generalization they aim to solve, the type of data shift they consider, the source by which this data shift originated, and the locus of the shift within the NLP modelling pipeline. We use our taxonomy to classify over 700 experiments, and we use the results to present an in-depth analysis that maps out the current state of generalization research in NLP and make recommendations for which areas deserve attention in the future.
AB - The ability to generalize well is one of the primary desiderata for models of natural language processing (NLP), but what ‘good generalization’ entails and how it should be evaluated is not well understood. In this Analysis we present a taxonomy for characterizing and understanding generalization research in NLP. The proposed taxonomy is based on an extensive literature review and contains five axes along which generalization studies can differ: their main motivation, the type of generalization they aim to solve, the type of data shift they consider, the source by which this data shift originated, and the locus of the shift within the NLP modelling pipeline. We use our taxonomy to classify over 700 experiments, and we use the results to present an in-depth analysis that maps out the current state of generalization research in NLP and make recommendations for which areas deserve attention in the future.
KW - Generalization in NLP
KW - Taxonomy of Generalization
KW - Data Shift Origins
KW - Natural Language Processing Evaluation
KW - Generalization Research Analysis
KW - Generalization in NLP
KW - Taxonomy of Generalization
KW - Data Shift Origins
KW - Natural Language Processing Evaluation
KW - Generalization Research Analysis
M3 - Paper
ER -