Linear Discriminative Learning: a competitive non-neural baseline for morphological inflection
Cheonkam Jeong, Dominic Schmitz, Akhilesh Kakolu Ramarao, Anna H. Stein, Kevin D. Tang · 2023
This paper presents our submission to the SIG-MORPHON 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 speakerand item-specifics considerably.