Subword models struggle with word learning, but surprisal hides it
Abstract
Character language models outperform subword language models in word learning and separate word learning from syntactic learning, suggesting they are better suited for modeling language acquisition.
We study word learning in subword and character language models with the psycholinguistic lexical decision task. While subword LMs struggle to discern words and non-words with high accuracy, character LMs solve this task easily and consistently. Furthermore, when comparing word learning and syntactic learning, both processes are separable in character LM where word learning predates syntactic learning, whereas these processes are simultaneous in subword LM. This raises questions about the adequacy of subword LMs for modeling language acquisition and positions character LMs as a viable alternative.
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