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        <title>Frame Identification as Categorization: Exemplars vs Prototypes in Embeddingland</title>
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    <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 for these variables, establishes a new state of the art in frame identification.</abstract>
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%0 Conference Proceedings
%T Frame Identification as Categorization: Exemplars vs Prototypes in Embeddingland
%A Sikos, Jennifer
%A Padó, Sebastian
%Y Dobnik, Simon
%Y Chatzikyriakidis, Stergios
%Y Demberg, Vera
%S Proceedings of the 13th International Conference on Computational Semantics - Long Papers
%D 2019
%8 May
%I Association for Computational Linguistics
%C Gothenburg, Sweden
%F sikos-pado-2019-frame
%X 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 for these variables, establishes a new state of the art in frame identification.
%R 10.18653/v1/W19-0425
%U https://aclanthology.org/W19-0425
%U https://doi.org/10.18653/v1/W19-0425
%P 295-306

Markdown (Informal)

[Frame Identification as Categorization: Exemplars vs Prototypes in Embeddingland](https://aclanthology.org/W19-0425) (Sikos & Padó, IWCS 2019)

• Frame Identification as Categorization: Exemplars vs Prototypes in Embeddingland (Sikos & Padó, IWCS 2019)

ACL

• Jennifer Sikos and Sebastian Padó. 2019. Frame Identification as Categorization: Exemplars vs Prototypes in Embeddingland. In Proceedings of the 13th International Conference on Computational Semantics - Long Papers, pages 295–306, Gothenburg, Sweden. Association for Computational Linguistics.