_:ne601e63ab8e94689bf24985783d1f300xb0
<http://schema.org/articleBody>
"When learning their native language  children acquire the meanings of words and sentences from highly ambiguous input without much explicit supervision  One possible learning mechanism is cross-situational learning  which has been successfully tested in laboratory experiments with children  Here we use Artificial Neural Networks to test if this mechanism scales up to more natural language and visual scenes using a large dataset of crowd-sourced images with corresponding descriptions  We evaluate learning using a series of tasks inspired by methods commonly used in laboratory studies of language acquisition  We show that the model acquires rich semantic knowledge both at the word-and sentence-level  mirroring the patterns and trajectory of learning in early childhood  Our work highlights the usefulness of low-level cooccurrence statistics across modalities in facilitating the early acquisition of higher-level semantic knowledge "
<https://www.mysciencework.com/publication/facets/keywords/%255BINFO.INFO-CL%255DComputer%2520Science%2520%255Bcs%255D%252FComputation%2520and%2520Language%2520%255Bcs.CL%255D>