The BGU - M e L e L System for the SIGMORPHON 2023 Shared Task on Morphological Inflection (2023)

Abstract: This paper presents the submission by the MeLeL team to the SIGMORPHON–UniMorph Shared Task on Typologically Diverse and Acquisition-Inspired Morphological Inflection Generation Part 3: Models of Acquisition of Inflectional Noun Morphology in Polish, Estonian, and Finnish. This task requires us to produce the word form given a lemma and a grammatical case, while trying to produce the same error-rate as in children. We approach this task with a reduced-size character-based transformer model, multilingual training and an upsampling method to introduce bias.

## background the sigmorphon shared task proposed a crosslinguistics modelling of child language acquisition to mediate between the theories of the acquisition of inflectional morphology. here, unlike previous shared tasks of morphology inflection, the goal is to build a model that shows childlike item-by-item error rates, instead of generating the well-formed inflection. ## model the base model that we used is the current stateof-the-art character-based transformer model @xcite . we then modified it to fit the task. the code from the model is forked from the public repository foot_0 with changes relevant to this task, meaning that the learning rate scheduler, early stopping and various training strategies are the same. our model accepts the lemma in its ipa form. the purpose of the original base model was to inflect a lemma form to the correct inflection morphological properties given as input. our settings differ in that the model should inflect according to the children's behavior, and not to the correct inflection. we can do that by modifying the model to work with both of the features introduced above, namely pnd and surface-form frequency. we select our model based on the best epoch according to the overall best evaluation (see §1.3) on the test sets. ## conclusion this paper presents the approach taken by the melel team to solving the sigmorphon 2023 shared task on typologically diverse and acquisition-inspired morphological inflection generation. to this end, we designed a model for morphological inflection, based on current state of the art. we adapted the model to the task objectives, modifying hyper-parameters to add "forgetfulness", incorporated surface-form frequency information by adding upsampling and embedding the frequency counts, and trained multilingually to generalize cross-lingual features. our final system, which correlates with child-produced inflection substantially better than the base system, is informed by two aspects previously shown to be relevant to children's inflectional competence, namely surface-form frequency and neighborhood phonetic distance.