A Morpheme-Aware Child-Inspired Language Model

AbstractMost tokenization methods in language models rely on subword units that lack explicit linguistic correspondence. In this work, we investigate the impact of using morpheme-based tokens in a small language model, comparing them to the widely used frequency-based method, BPE. We apply the morpheme-based tokenization method to both 10-million and 100-million word datasets from the BabyLM Challenge. Our results show that using a morphological tokenizer improves EWoK (basic world knowledge) performance by around 20% and entity tracking by around 40%, highlighting the impact of morphological information in developing smaller language models. We also apply curriculum learning, in which morphological information is gradually introduced during training, mirroring the vocabulary-building stage in infants that precedes morphological processing. The results are consistent with previous research: curriculum learning yields slight improvements for some tasks, but performance degradation in others.