@inproceedings{jeong-etal-2023-linear,
title = "Linear Discriminative Learning: a competitive non-neural baseline for morphological inflection",
author = "Jeong, Cheonkam and
Schmitz, Dominic and
Kakolu Ramarao, Akhilesh and
Stein, Anna and
Tang, Kevin",
editor = {Nicolai, Garrett and
Chodroff, Eleanor and
Mailhot, Frederic and
{\c{C}}{\"o}ltekin, {\c{C}}a{\u{g}}r{\i}},
booktitle = "Proceedings of the 20th SIGMORPHON workshop on Computational Research in Phonetics, Phonology, and Morphology",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.sigmorphon-1.16",
doi = "10.18653/v1/2023.sigmorphon-1.16",
pages = "138--150",
abstract = "This paper presents our submission to the SIGMORPHON 2023 task 2 of Cognitively Plausible Morphophonological Generalization in Korean. We implemented both Linear Discriminative Learning and Transformer models and found that the Linear Discriminative Learning model trained on a combination of corpus and experimental data showed the best performance with the overall accuracy of around 83{\%}. We found that the best model must be trained on both corpus data and the experimental data of one particular participant. Our examination of speaker-variability and speaker-specific information did not explain why a particular participant combined well with the corpus data. We recommend Linear Discriminative Learning models as a future non-neural baseline system, owning to its training speed, accuracy, model interpretability and cognitive plausibility. In order to improve the model performance, we suggest using bigger data and/or performing data augmentation and incorporating speaker- and item-specifics considerably.",
}
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%0 Conference Proceedings
%T Linear Discriminative Learning: a competitive non-neural baseline for morphological inflection
%A Jeong, Cheonkam
%A Schmitz, Dominic
%A Kakolu Ramarao, Akhilesh
%A Stein, Anna
%A Tang, Kevin
%Y Nicolai, Garrett
%Y Chodroff, Eleanor
%Y Mailhot, Frederic
%Y Çöltekin, Çağrı
%S Proceedings of the 20th SIGMORPHON workshop on Computational Research in Phonetics, Phonology, and Morphology
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F jeong-etal-2023-linear
%X This paper presents our submission to the SIGMORPHON 2023 task 2 of Cognitively Plausible Morphophonological Generalization in Korean. We implemented both Linear Discriminative Learning and Transformer models and found that the Linear Discriminative Learning model trained on a combination of corpus and experimental data showed the best performance with the overall accuracy of around 83%. We found that the best model must be trained on both corpus data and the experimental data of one particular participant. Our examination of speaker-variability and speaker-specific information did not explain why a particular participant combined well with the corpus data. We recommend Linear Discriminative Learning models as a future non-neural baseline system, owning to its training speed, accuracy, model interpretability and cognitive plausibility. In order to improve the model performance, we suggest using bigger data and/or performing data augmentation and incorporating speaker- and item-specifics considerably.
%R 10.18653/v1/2023.sigmorphon-1.16
%U https://aclanthology.org/2023.sigmorphon-1.16
%U https://doi.org/10.18653/v1/2023.sigmorphon-1.16
%P 138-150
Markdown (Informal)
[Linear Discriminative Learning: a competitive non-neural baseline for morphological inflection](https://aclanthology.org/2023.sigmorphon-1.16) (Jeong et al., SIGMORPHON 2023)
ACL