@inproceedings{sikos19:_frame_ident_categ,
  abstract = {Categorization is a central capability of human cognition, and a number of theories have been developed to account for properties of categorization. Even though many tasks in semantics also involve categorization of some kind, theories of categorization do not play a major role in contemporary research in computational linguistics. This paper follows the idea that embedding-based models of semantics lend themselves well to being formulated in terms of classical categorization theories. The benefit is a space of model families that enables (a) the formulation of hypotheses about the impact of major design decisions, and (b) a transparent assessment of these decisions. We instantiate this idea on the task of frame-semantic frame identification. We define four models that cross two design variables: (a) the choice of prototype vs. exemplar categorization, corresponding to different degrees of generalization applied to the input, and (b) the presence vs. absence of a fine-tuning step, corresponding to generic vs. task-adaptive categorization. We find that for frame identification, generalization and task-adaptive categorization both yield substantial benefits. Our prototype-based, fine-tuned model, which combines the best choices over these variables, establishes a new state-of-the-art in frame identification.},
  added-at = {2019-03-01T14:58:32.000+0100},
  address = {Gothenburg, Sweden},
  author = {Sikos, Jennifer and Padó, Sebastian},
  biburl = {https://puma.ub.uni-stuttgart.de/bibtex/230f75b3516941ba88708c68dd6dd1133/sp},
  booktitle = {Proceedings of IWCS},
  interhash = {0192c079f2d6f24c98681a81279fdfe5},
  intrahash = {30f75b3516941ba88708c68dd6dd1133},
  keywords = {conference myown},
  pages = {295--306},
  timestamp = {2019-06-06T08:07:38.000+0200},
  title = {Frame Identification as Categorization: Exemplars vs Protoypes in Embeddingland},
  url = {https://aclweb.org/anthology/papers/W/W19/W19-0425/},
  year = 2019
}