Child-directed speech is statistically optimized for meaning extraction

Abstract

The way infants manage to extract meaning from the speech stream when learning their first language is a highly complex adaptive behavior. This behavior chiefly relies on the ability to extract information from speech they hear and combine it with the external environment they encounter. However, little is known about the underlying distribution of information in speech that conditions this ability. Here we examine properties of this distribution that support meaning extraction in three different types of speech: child-directed speech, adult conversation, and, as a control, written language. We find that verb meanings in child-directed speech can already be successfully extracted from simple co-occurrences of neighboring words, whereas meaning extraction in the other types of speech fundamentally requires access to more complex structural relations between neighboring words. These results suggest that child-directed speech is ideally shaped for a learner who has not yet mastered the structural complexity of her language and therefore mainly relies on distributional learning mechanisms to develop an understanding of linguistic meanings.

Adults adapt to child speech in semantic use

“…Hence, semantic nuances of co-occurring words are especially important for the inference of lexical causatives. Moreover, it has been suggested that lexical causatives can be successfully discriminated from non-causatives with the help of distributional learning algorithms (You et al, 2020). We can thus pinpoint the distributional semantics of lexical causatives to examine the semantic coordination between CDS and CS.…”

“…The generated vectors have proven efficient in capturing semantic regularities (Mikolov et al, 2013). Particularly, it is shown that word embeddings trained on adjacent raw contexts in CDS can facilitate causative discrimination (You et al, 2020).…”