Linear Discriminative Learning: a competitive non-neural baseline for morphological inflection
Cheonkam Jeong, Dominic Schmitz, Akhilesh Kakolu Ramarao, Anna Stein, Kevin Tang
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.- Anthology ID:
- 2023.sigmorphon-1.16
- Volume:
- Proceedings of the 20th SIGMORPHON workshop on Computational Research in Phonetics, Phonology, and Morphology
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Venue:
- SIGMORPHON
- SIG:
- SIGMORPHON
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 138–150
- Language:
- URL:
- https://aclanthology.org/2023.sigmorphon-1.16
- DOI:
- Cite (ACL):
- Cheonkam Jeong, Dominic Schmitz, Akhilesh Kakolu Ramarao, Anna Stein, and Kevin Tang. 2023. Linear Discriminative Learning: a competitive non-neural baseline for morphological inflection. In Proceedings of the 20th SIGMORPHON workshop on Computational Research in Phonetics, Phonology, and Morphology, pages 138–150, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Linear Discriminative Learning: a competitive non-neural baseline for morphological inflection (Jeong et al., SIGMORPHON 2023)
- PDF:
- https://preview.aclanthology.org/paclic-22-ingestion/2023.sigmorphon-1.16.pdf