Transferring Learned Models of Morphological Analogy

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Journal Title
Journal ISSN
Volume Title
Conference article
Date
2022
Major/Subject
Mcode
Degree programme
Language
en
Pages
16
14-29
Series
CEUR Workshop Proceedings, Volume 3389
Abstract
Analogical proportions are statements of the form “A is to B as C is to D”, which have been extensively studied in morphology. Recent advances on learning models of analogy from quadruples pave the way for data-driven modeling and analysis of analogy. In morphology, recent work introduces a neural network classifier for morphological analogies (ANNc). In this paper, we study the transferability of ANNc across different axiomatic settings to show the importance of the data augmentation in the modeling of analogy. We also provide experimental results on transfer between two morphology datasets (Sigmorphon2016 and Sigmorphon2019) and between more than 27 languages to draw parallels between transfer performance and proximity between language families.
Description
Funding Information: Experiments presented in this paper were carried out using computational clusters equipped with GPU from the Grid’5000 testbed (see https://www.grid5000.fr). This research was partially supported by TAILOR, a project funded by EU Horizon 2020 research and innovation program under GA No 952215, and the Inria Project Lab “Hybrid Approaches for Interpretable AI” (HyAIAI). Publisher Copyright: © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). CEUR Workshop Proceedings (CEUR-WS.org)
Keywords
Analogy detection, Morphological analogies, Transfer
Other note
Citation
Marquer , E , Murena , P A & Couceiro , M 2022 , ' Transferring Learned Models of Morphological Analogy ' , CEUR Workshop Proceedings , vol. 3389 , pp. 14-29 .