Constructional ‘scene encoding’ and acquisition: Mothers’ use of argument structure constructions in English child-directed speech
Construction-based language models assume that grammar is meaningful and learnable from experience. Focusing on five of the most elementary argument structure constructions of English, a large-scale corpus study of child-directed speech (CDS)...
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Construction-based language models assume that grammar is meaningful and learnable from experience. Focusing on five of the most elementary argument structure constructions of English, a large-scale corpus study of child-directed speech (CDS) investigates exactly which meanings/functions are associated with these patterns in CDS, and whether they are indeed specially indicated to children by their caretakers (as suggested by previous research, cf. Goldberg, Casenhiser and Sethuraman 2004). Collostructional analysis (Stefanowitsch and Gries 2003) is employed to uncover significantly attracted verb-construction combinations, and attracted pairs are classified semantically in order to systematise the attested usage patterns of the target constructions. The results indicate that the structure of the input may aid learners in making the right generalisations about constructional usage patterns, but such scaffolding is not strictly necessary for construction learning: not all argument structure constructions are coherently semanticised to the same extent (in the sense that they designate a single schematic event type of the kind envisioned in Goldberg’s [1995] ‘scene encoding hypothesis’), and they also differ in the extent to which individual semantic subtypes predominate in learners’ input
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Hvordan lærer menneskebørn og humanoide robotter sprog? Nye Forskningshorisonter i studiet af sprogtilegnelse og kognitiv udvikling
We taught a humanoid robot a number of different actions involving a number of different objects (e.g., touching a green object, moving a red object etc.) alongside a number of simplified linguistic labels for these behaviours (e.g., ‘touch-green’,...
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We taught a humanoid robot a number of different actions involving a number of different objects (e.g., touching a green object, moving a red object etc.) alongside a number of simplified linguistic labels for these behaviours (e.g., ‘touch-green’, ‘move-red’ etc.). The robot managed to learn the associations between the behaviours and their linguistic labels, and it succeeded in recognising the compositional structure of the behaviours and their associated linguistic descriptions (ACTION/VERB+OBJECT/NOUN). Moreover, it was able to generalise the learned instructions to novel, previously untrained action+object-combinations (e.g., touch-red). This corresponds to the task of learning and decomposing so-called ‘holophrases’ in early child language acquisition.
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