@inproceedings{shcherbakov-vylomova-2023-topological,
title = "Does Topological Ordering of Morphological Segments Reduce Morphological Modeling Complexity? A Preliminary Study on 13 Languages",
author = "Shcherbakov, Andreas and
Vylomova, Ekaterina",
editor = "Beinborn, Lisa and
Goswami, Koustava and
Murado{\u{g}}lu, Saliha and
Sorokin, Alexey and
Kumar, Ritesh and
Shcherbakov, Andreas and
Ponti, Edoardo M. and
Cotterell, Ryan and
Vylomova, Ekaterina",
booktitle = "Proceedings of the 5th Workshop on Research in Computational Linguistic Typology and Multilingual NLP",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.sigtyp-1.12",
doi = "10.18653/v1/2023.sigtyp-1.12",
pages = "120--125",
abstract = "Generalization to novel forms and feature combinations is the key to efficient learning. Recently, Goldman et al. (2022) demonstrated that contemporary neural approaches to morphological inflection still struggle to generalize to unseen words and feature combinations, even in agglutinative languages. In this paper, we argue that the use of morphological segmentation in inflection modeling allows decomposing the problem into sub-problems of substantially smaller search space. We suggest that morphological segments may be globally topologically sorted according to their grammatical categories within a given language. Our experiments demonstrate that such segmentation provides all the necessary information for better generalization, especially in agglutinative languages.",
}
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<abstract>Generalization to novel forms and feature combinations is the key to efficient learning. Recently, Goldman et al. (2022) demonstrated that contemporary neural approaches to morphological inflection still struggle to generalize to unseen words and feature combinations, even in agglutinative languages. In this paper, we argue that the use of morphological segmentation in inflection modeling allows decomposing the problem into sub-problems of substantially smaller search space. We suggest that morphological segments may be globally topologically sorted according to their grammatical categories within a given language. Our experiments demonstrate that such segmentation provides all the necessary information for better generalization, especially in agglutinative languages.</abstract>
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%0 Conference Proceedings
%T Does Topological Ordering of Morphological Segments Reduce Morphological Modeling Complexity? A Preliminary Study on 13 Languages
%A Shcherbakov, Andreas
%A Vylomova, Ekaterina
%Y Beinborn, Lisa
%Y Goswami, Koustava
%Y Muradoğlu, Saliha
%Y Sorokin, Alexey
%Y Kumar, Ritesh
%Y Shcherbakov, Andreas
%Y Ponti, Edoardo M.
%Y Cotterell, Ryan
%Y Vylomova, Ekaterina
%S Proceedings of the 5th Workshop on Research in Computational Linguistic Typology and Multilingual NLP
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F shcherbakov-vylomova-2023-topological
%X Generalization to novel forms and feature combinations is the key to efficient learning. Recently, Goldman et al. (2022) demonstrated that contemporary neural approaches to morphological inflection still struggle to generalize to unseen words and feature combinations, even in agglutinative languages. In this paper, we argue that the use of morphological segmentation in inflection modeling allows decomposing the problem into sub-problems of substantially smaller search space. We suggest that morphological segments may be globally topologically sorted according to their grammatical categories within a given language. Our experiments demonstrate that such segmentation provides all the necessary information for better generalization, especially in agglutinative languages.
%R 10.18653/v1/2023.sigtyp-1.12
%U https://aclanthology.org/2023.sigtyp-1.12
%U https://doi.org/10.18653/v1/2023.sigtyp-1.12
%P 120-125
Markdown (Informal)
[Does Topological Ordering of Morphological Segments Reduce Morphological Modeling Complexity? A Preliminary Study on 13 Languages](https://aclanthology.org/2023.sigtyp-1.12) (Shcherbakov & Vylomova, SIGTYP 2023)
ACL