Cross-linguistic effects in bilingual first-language acquisition
Cross-linguistic effects are a central topic in bilingual first-language acquisition.
Artificial learners and L1-L2 interactions
Artificial learners can help investigate L1-L2 interactions by enabling controlled comparisons across language combinations and learning conditions.
Training bilingual language models
Recent work explores this direction by training bilingual language models under developmentally plausible constraints.
Human and model learners
However, human and model learners still diverge in fundamental ways, with one major difference being input modality: children learn primarily from spoken input, whereas language models are typically trained on orthographic text.
Training models on phonemic representations
To reduce this gap, researchers have trained models on phonemic representations of speech.
Bilingual BabyLMs with phonemic input
In this work, we combine these research directions to train bilingual BabyLMs with phonemic input.
Fixed L2 and varying L1
We keep English fixed as the L2 and vary the L1 across German, Swedish, Persian, and Basque, selected to represent contrasting combinations of syntactic and phoneme-inventory distance from English.
Results and observations
Our results show stronger L1-related variation in grammatical learning trajectories under phonemic than orthographic input, while early lexical differences align with phoneme-inventory similarity.