Diversifying language models for lesser-studied languages and language-usage contexts: A case of second language Korean
Submission Type: Regular Long Paper
Submission Track: Multilinguality and Linguistic Diversity
Submission Track 2: Phonology, Morphology, and Word Segmentation
Keywords: Multilinguality, DEI, NLP applications, L2 Korean, Morpheme parsing/tagging
TL;DR: This study explores the applicability of existing morpheme parsers/taggers to lesser-studied languages and contexts (L2 Korean) by training a neural-network model on diverse L2 Korean data and evaluating its parsing/tagging performance.
Abstract: This study investigates the extent to which currently available morpheme parsers/taggers apply to lesser-studied languages and language-usage contexts, with a focus on second language (L2) Korean. We pursue this inquiry by (1) training a neural-network model (pre-trained on first language [L1] Korean data) on varying L2 datasets and (2) measuring its morpheme parsing/POS tagging performance on L2 test sets from both the same and different sources of the L2 train sets. Results show that the L2 trained models generally excel in domain-specific tokenization and POS tagging compared to the L1 pre-trained baseline model. Interestingly, increasing the size of the L2 training data does not lead to improving model performance consistently.
Submission Number: 787
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