BibTeX
@InProceedings{sikos19:_frame_ident_categ,
author = {Jennifer Sikos and Sebastian Padó},
title = {Frame Identification as Categorization: Exemplars vs Protoypes in {Embeddingland}},
keywords = {conference myown},
booktitle = {Proceedings of IWCS},
year = 2019,
url = {https://aclweb.org/anthology/papers/W/W19/W19-0425/},
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.},
address = {Gothenburg, Sweden}}